Environmental Performance Index at the Provincial Level for China 2006–2011

Zuo, Xuan; Hua, H.; Dong, Zhanfeng; Hao, Chunxu

doi:10.1016/j.ecolind.2016.12.016

Cited by 56 publications

(28 citation statements)

References 21 publications

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“…An environmental performance index (EPI) can serve as a tool to represent the quality of life across various activities. At global level, the global environmental performance index (GEPI) has been established, which includes comprehensive indicators for measuring and tracking the environmental performance of a country under two categories—ecosystem and health 1,2 . Since 2006, the GEPI has been quantified and ranked regionally for both categories across 180 countries.…”

Section: Introductionmentioning

confidence: 99%

A new environmental performance index based on the carbon footprint, VOC emissions, and waste in a printing house

Hansuebsai

Kaosod

Kanchanasing

2020

Engineering Reports

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An environmental performance index (EPI) is introduced to evaluate the activities of a printing house based on three impacts: carbon footprint, volatile organic compound emissions, and waste. As the main causes of environmental degradation are the operation of machinery and usage of materials, we aimed to integrate the three impacts to interpret the EPI or the environmental friendliness of printing houses. Our results demonstrate that environmental awareness increased with improvements to the EPI, following the modification of printing processes and choices of printing materials. Various scenarios, including pre-press, press, and post-press, as well as printing materials, are proposed to predict trends and the possibility of increasing the EPI. Press management and materials and equipment changes have the greatest influence on the improvement of the EPI.

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Section: Introductionmentioning

confidence: 99%

A new environmental performance index based on the carbon footprint, VOC emissions, and waste in a printing house

Hansuebsai

Kaosod

Kanchanasing

2020

Engineering Reports

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“…To counteract this arbitrariness, researchers are working to improve the selection process. For example, several authors have proposed classifying indicators based on the Driver-Pressure-State-Impact-Response (DPSIR) [16] methodology, which provides justification for the inclusion of various indicators [17,18]. Other work has proposed a more computational approach based on the Entangled Economy Model [19].…”

Section: Introductionmentioning

confidence: 99%

Assessing Global Environmental Sustainability Via an Unsupervised Clustering Framework

et al. 2020

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The importance of sustainable development has risen in recent years due to the significant number of people affected by lack of access to essential resources as well as the need to prepare for and adapt to intensifying climate change and rapid urbanization. Modeling frameworks capable of effectively assessing and tracking sustainability lie at the heart of creating effective policies to address these issues. Conventional frameworks, such as the Environmental Performance Index (EPI), that support such policies often involve ranking countries based on a weighted sum of a number of relevant environmental metrics. However, the selection and weighing processes are often biased. Moreover, the ranking process fails to provide policymakers with possible avenues to improve their country’s environmental sustainability. This study aimed to address these gaps by proposing a novel data-driven framework to assess the environmental sustainability of countries objectively by leveraging unsupervised learning theory. Specifically, this framework harnesses a clustering technique known as Self-Organized Maps to group countries based on their characteristic environmental performance metrics and track progression in terms of shifts within clusters over time. The results support the hypothesis that the inconsistencies in the EPI calculation can lead to misrepresentations of the relative sustainability of countries over time. The proposed framework, which does not rely on ranking or data transformations, enables countries to make more informed decisions by identifying effective and specific pathways towards improving their environmental sustainability.

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“…pollutant concentration distribution, exposure and its effects, vulnerability assessment; therefore, they constitute a very powerful tool to support policy-making in complex environmental risk assessment framework (Lahr and Kooistra, 2010;Li et al, 2015). Thus, Bién et al (2005) applied their own Health Index/Risk Evaluation Tool to map the spatio-temporal probability of cancer cases in an area of soils contaminated with benzene and Zuo et al (2017) used an Environmental Performance Index to evaluate the environmental performance of provinces in China between 2006 and 2011. Moreover, Entropy indexes have recently been used to provide pivotal information for mitigation strategies against desertification (Zambon et al, 2017) and Moreno-Jiménez et al (2011), proposed a new methodology for assessing the site-specific environmental impact of contaminants at a local scale.…”

Section: Introductionmentioning

confidence: 99%

Developing a new Bayesian Risk Index for risk evaluation of soil contamination

Albuquerque

Gerassis

Sierra

et al. 2017

Science of The Total Environment

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Industrial and agricultural activities heavily constrain soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment alike. In this regard, the identification of areas that require remediation is crucial. In the herein research a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain). Then a stratified systematic sampling method was used at short, medium, and long distances from each zone to obtain a representative picture of the total variability of the selected attributes. The information was then combined in four risk classes (Low, Moderate, High, Remediation) following reference values from several sediment quality guidelines (SQGs). A Bayesian analysis, inferred for each zone, allowed the characterization of PTEs correlations, the unsupervised learning network technique proving to be the best fit. Based on the Bayesian network structure obtained, Pb, As and Mn were selected as key contamination parameters. For these 3 elements, the conditional probability obtained was allocated to each observed point, and a simple, direct index (Bayesian Risk Index-BRI) was constructed as a linear rating of the pre-defined risk classes weighted by the previously obtained probability. Finally, the BRI underwent geostatistical modeling. One hundred Sequential Gaussian Simulations (SGS) were computed. The Mean Image and the Standard Deviation maps were obtained, allowing the definition of High/Low risk clusters (Local G clustering) and the computation of spatial uncertainty. High-risk clusters are mainly distributed within the area with the highest altitude (agriculture/livestock) showing an associated low spatial uncertainty, clearly indicating the need for remediation. Atmospheric emissions, mainly derived from the metallurgical industry, contribute to soil contamination by PTEs.

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Environmental Performance Index at the Provincial Level for China 2006–2011

Cited by 56 publications

References 21 publications

A new environmental performance index based on the carbon footprint, VOC emissions, and waste in a printing house

A new environmental performance index based on the carbon footprint, VOC emissions, and waste in a printing house

Assessing Global Environmental Sustainability Via an Unsupervised Clustering Framework

Developing a new Bayesian Risk Index for risk evaluation of soil contamination

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