Environmental Assessment of European Union Countries

Hsieh, Jin-chi; Lu, Ching‐Cheng; Liu, Ying; Chiu, Yung‐ho; Xu, Ya-sue

doi:10.3390/en12020295

Cited by 15 publications

(16 citation statements)

References 37 publications

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“…The abovementioned wastewater discharge (WD), COD, other major pollutants (OMP) are bad outputs. We refer to Hsieh et al and Ma et al [26,27] to adjust bad outputs, as good output variables, and utilize the maximum emission value of each bad output in all DMUs as the base; whereby the adjustment value equals (maximum value of DMUs -original emissions value of each DMU) + 1 to become good outputs. These results illustrate the relationships among rapid economic growth, water resources' use policy, and environmental degradation in China at the provincial level, thus spurring local governments to make a careful assessment of appropriate related policies when actively targeting economic development at the same time.…”

Section: Introductionmentioning

confidence: 99%

Assessing China’s Use Efficiency of Water Resources from the Resampling Super Data Envelopment Analysis Approach

Hsieh

Chiu

2019

Water

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Water resources’ use efficiency is an important issue under China’s rapid economic growth. This is because some provinces’ economic development may be delayed due to lack of adequate water resources. Whereas, high economically developed provinces may overuse water resources in order to achieve their economic goals; while also creating a large amount of pollutants. To assess water resources’ use efficiency from the resampling super data envelopment analysis (DEA) approach, our research comprehensively utilizes the following as inputs and outputs: (1) water resources: supply of water (SW), per capita water consumption (PCWC), and total water resources (TWR); (2) economic development: gross domestic product (GDP); (3) environmental issues: governance wastewater investment (GWI), wastewater discharge (WD), chemical oxygen demand (COD), and other major pollutants (OMP). The results show that Tibet, Beijing, Guangdong, Qinghai, Shandong, Sichuan, Yunnan, Tianjin, Jiangsu, and Henan have relatively good water resources’ use efficiency with efficiency values larger than 1. The best efficiency is in 2015, while the worst is in 2017. Water resources’ use efficiency shows significant regional differences in 2013–2017, with the best average efficiency value in southwest China (1.4355) and the worst in north China (0.2987). The results of the Wilcoxon test present that PCWC, GDP, COD, and OMP exhibit very significant differences, PN and WD have significant differences, and SW and TWR have no significant influence. These results imply that China’s regional governments must formulate a better water resource strategy based on the water resource distribution of each region. Lastly, the emissions of environmental pollutants must be strictly monitored.

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Section: Introductionmentioning

confidence: 99%

Assessing China’s Use Efficiency of Water Resources from the Resampling Super Data Envelopment Analysis Approach

Hsieh

Chiu

2019

Water

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“…Li et al [69] argued that fixed asset stock is a significant indicator of environmental efficiency and should be adopted as a carry-over indicator to identify the performance gap among decision-making units. Inspired by Li et al [69,70] and Hsieh et al [71], the study adopts fixed assets as a carry-over indicator. As shown in Table 8, fixed assets adjustment values are negative for all investigated African countries except for Togo and Namibia's efficient countries.…”

Section: Ecological Efficiency Improvements In Africa From 2010 To 2016mentioning

confidence: 99%

Towards Ecological Sustainability: Assessing Dynamic Total-Factor Ecology Efficiency in Africa

Amowine

Boamah

et al. 2021

IJERPH

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Ecological footprint (EF) and human development index (HDI) are two critical indicators for assessing sustainable development worldwide. Past studies in Africa have ignored dynamic sustainable total-factor ecological efficiency (DSTFEE) assessment. This present study proffers a novel dynamic sustainable total-factor ecological efficiency (DSTFEE) that comprehensively assesses the ecological efficiency among 44 sampled African economies from 2010 to 2016. Our study incorporates EF and HDI in the model. Second, the study evaluates regional DSTFEE heterogeneity efficiency as well as the technological gap efficiency in Africa. Further, projection analysis is done to offer a viable solution path to address the inefficient African countries. Third, the study investigates the determinants of ecological efficiency using the bootstrap truncation regression technique. The results from the implemented models are as follows: first, the DSTFEE for the 44 sampled African countries is very low (0.403), indicating enormous potential for improvement. Second, the heterogeneity of DSTFEE across the five Africa regional blocs is evident. The southern bloc had the highest efficiency score, followed by the northern, central, western, and eastern regions. The technology gap ratio also reveals a massive gap among the five Africa regional blocs. Third, the bootstrap truncation regression results established a U-shape nexus between growth and DSTFEE in Africa. REC and trade openness is positively corrected to DSTFEE for African countries. In contrast, financial development, foreign direct investment (FDI), and urbanization impede dynamic ecological efficiency in Africa. The study’s results equip African countries with adequate knowledge of their ecological efficiency situation and provide them a viable path to improve environmental efficiency, thereby boosting their ecological sustainability.

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“…Cui et al (2014) found that energy efficiency is mostly driven by investments into energy technologies research and tax exemptions for technology companies [45]. Based on the analysis of 28 European countries, Hsieh et al (2019) recommend that "the EU's strategy for environmental energy improvement should be to pay attention to the benefits of renewable energy utilization, reducing GHG emissions, and enhancing the development of renewable energy utilization to help achieve the goal of lower GHG emissions" [48]. Apergis et al (2015) show that capital-intensive countries are more energy efficient than labor-intensive ones [34].…”

Section: Literature Review-energy-related Country Comparisons With Damentioning

confidence: 99%

“…Out of the 25 studies analyzed (including the present one), 15 of them (60%) use the same set of inputs: labor force, capital stock and energy consumption [34,39,40,42,44,[47][48][49][50][52][53][54]56,57,59]. Furthermore, out of these 15 studies, four consider only the GDP as an output [44,49,53,56], while the rest consider the GDP as a desirable output and GHG emissions as an undesirable output [34,39,40,42,47,48,50,52,54,57,59]. Within DEA country comparisons, efficiency is, therefore, usually measured as a minimization of the labor force, capital stock, and energy consumption (inputs) in order to maximize the GDP (desirable output) and minimize the GHG emissions (undesirable output).…”

Section: Literature Review-energy-related Country Comparisons With Damentioning

confidence: 99%

Quantifying Electricity Supply Resilience of Countries with Robust Efficiency Analysis

et al. 2020

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The interest in studying energy systems’ resilience is increasing due to a rising awareness of the importance of having a secure energy supply. This growing trend is a result of a series of recent disruptions, among others also affecting electricity systems. Therefore, it is of crucial importance for policymakers to determine whether their country has a resilient electricity supply. Starting from a set of 12 indicators, this paper uses data envelopment analysis (DEA) to comprehensively evaluate the electricity supply resilience of 140 countries worldwide. Two DEA models are applied: (1) the original ratio-based Charnes, Cooper, and Rhodes (CCR) model and (2) a novel hybrid framework for robust efficiency analysis incorporating linear programming and Monte Carlo simulations. Results show that the CCR model deems 31 countries as efficient and hence lacks the capability to differentiate them. Furthermore, the CCR model considers only the best weight vectors for each country, which are not necessarily representative of the overall performance of the countries. The robustness analysis explores these limitations and identifies South Korea, Singapore and Canada as the most resilient countries. Finally, country analyses are conducted, where Singapore’s and Japan’s performances and improvement potentials are discussed.

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Environmental Assessment of European Union Countries

Cited by 15 publications

References 37 publications

Assessing China’s Use Efficiency of Water Resources from the Resampling Super Data Envelopment Analysis Approach

Assessing China’s Use Efficiency of Water Resources from the Resampling Super Data Envelopment Analysis Approach

Towards Ecological Sustainability: Assessing Dynamic Total-Factor Ecology Efficiency in Africa

Quantifying Electricity Supply Resilience of Countries with Robust Efficiency Analysis

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