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Analysis of the spatial characteristics and influencing factors of agricultural eco-efficiency: evidence from Anhui Province, China, during the period 2011-2018

Wu Y, Rahman RA, Yu Q

Environ Monit Assess, 2022 Feb 08;194(3):154.
PMID: 35132444 DOI: 10.1007/s10661-022-09817-9

Sustainable agriculture is important for preserving environmental health and simultaneously gaining economic profits while maintaining social and economic equity. One way to evaluate sustainable agriculture is by studying agricultural eco-efficiency (AEE). Hence, this study constructed a data-driven method to evaluate and optimize AEE with the aim of providing a basis for improving the sustainable development of regional agriculture. Sixteen cities in Anhui Province, China, were considered in the study, and the variables used were agricultural resource inputs, environmental pollution, and agricultural economic development. Agricultural non-point source pollution (NPSP) emissions were considered the undesired output to build an AEE evaluation index system. Furthermore, a data envelopment analysis (DEA) model was established to analyse AEE from the static and dynamic perspectives. The spatial development and the temporal and spatial characteristics of AEE were also analysed. In addition, we applied a random effect (RE) panel Tobit model to quantitatively analyse the influencing factors of AEE from the input perspective and then proposed reasonable suggestions for improving the sustainable development of regional agriculture. Our findings show that the overall agricultural development in the 16 cities in Anhui Province has been continuously improving, even though there is an agglomeration of spatial development in some regions. In conclusion, this study provides suggestions and references for policy makers and agricultural practitioners regarding how to improve regional AEE and promote the sustainable development of the regional agricultural economy.

Matched MeSH terms: Efficiency
Fulltext Cost of Illness Analysis of Type 2 Diabetes Mellitus: The Findings from a Lower-Middle Income Country

Butt MD, Ong SC, Wahab MU, Rasool MF, Saleem F, Hashmi A, et al.

Int J Environ Res Public Health, 2022 Oct 02;19(19).
PMID: 36231911 DOI: 10.3390/ijerph191912611

BACKGROUND: Diabetes is a major chronic illness that negatively influences individuals and society. Therefore, this research aimed to analyze and evaluate the cost associated with diabetes management, specific to the Pakistani Type 2 diabetes population. Research scheme and methods: A survey randomly collected information and data from diabetes patients throughout Pakistan out-patient clinics. Direct and indirect costs were evaluated, and data were analyzed with descriptive and inferential statistics.
RESULTS: An overall of 1839 diabetes patients participated in the study. The results have shown that direct and indirect costs are positively associated with the participants' socio-demographic characteristics, except for household income and educational status. The annual total cost of diabetes care was USD 740.1, amongst which the share of the direct cost was USD 646.7, and the indirect cost was USD 93.65. Most direct costs comprised medicine (USD 274.5) and hospitalization (USD 319.7). In contrast, the productivity loss of the patients had the highest contribution to the indirect cost (USD 81.36).
CONCLUSION: This study showed that direct costs significantly contributed to diabetes's overall cost in Pakistan and overall diabetes management estimated to be 1.67% (USD 24.42 billion) of the country's total gross domestic product. The expense of medications and hospitalization mostly drove the direct cost. Additionally, patients' loss of productivity contributed significantly to the indirect cost. It is high time for healthcare policymakers to address this huge healthcare burden. It is time to develop a thorough diabetes management plan to be implemented nationwide.

Matched MeSH terms: Efficiency
Prediction of work productivity outcomes by identifying critical risk factors among garment industry workers

Javed I, Md Dawal SZ, Nukman Y, Ahmad A

Int J Occup Saf Ergon, 2022 Dec;28(4):2238-2249.
PMID: 34556003 DOI: 10.1080/10803548.2021.1984673

Work productivity is one of the most important economic measures in the manufacturing industry. However, the physical, psychosocial and individual risk factors of an industrial work environment affect workers' physical or mental health, resulting in work productivity loss, absenteeism and presenteeism. Therefore, this study aims to identify the most critical risk factors and develop statistical models for predicting work productivity loss, absenteeism and presenteeism of garment industry workers. A sample of 224 sewing machine operators was taken for data collection through observation and self-reported studies. The results indicated that the average work productivity loss, absenteeism and presenteeism was 38.21, 2.35 and 37.23%, respectively. Finally, the statistical models of work productivity loss, absenteeism and presenteeism was developed using multiple linear regression with precision of 69.9, 53.7 and 84.0%, respectively. Hence, this study will help garment industries to improve their work productivity by taking initiatives based on the developed models.

Matched MeSH terms: Efficiency*
Fulltext The COVID-19 impact on air condition usage: a shift towards residential energy saving

Aliero MS, Pasha MF, Toosi AN, Ghani I

Environ Sci Pollut Res Int, 2022 Dec;29(57):85727-85741.
PMID: 35001275 DOI: 10.1007/s11356-021-17862-z

The enforcement of the Movement Control Order to curtail the spread of COVID-19 has affected home energy consumption, especially HVAC systems. Occupancy detection and estimation have been recognized as key contributors to improving building energy efficiency. Several solutions have been proposed for the past decade to improve the precision performance of occupancy detection and estimation in the building. Environmental sensing is one of the practical solutions to detect and estimate occupants in the building during uncertain behavior. However, the literature reveals that the performance of environmental sensing is relatively poor due to the poor quality of the training dataset used in the model. This study proposed a smart sensing framework that combined camera-based and environmental sensing approaches using supervised learning to gather standard and robust datasets related to indoor occupancy that can be used for cross-validation of different machine learning algorithms in formal research. The proposed solution is tested in the living room with a prototype system integrated with various sensors using a random forest regressor, although other techniques could be easily integrated within the proposed framework. The primary implication of this study is to predict the room occupation through the use of sensors providing inputs into a model to lower energy consumption. The results indicate that the proposed solution can obtain data, process, and predict occupant presence and number with 99.3% accuracy. Additionally, to demonstrate the impact of occupant number in energy saving, one room with two zones is modeled each zone with air condition with different thermostat controller. The first zone uses IoFClime and the second zone uses modified IoFClime using a design-builder. The simulation is conducted using EnergyPlus software with the random simulation of 10 occupants and local climate data under three scenarios. The Fanger model's thermal comfort analysis shows that up to 50% and 25% energy can be saved under the first and third scenarios.

Matched MeSH terms: Efficiency
Fulltext Data envelopment analysis for ambulance services of different service providers in urban and rural areas in Ministry of Health Malaysia

Mohd Hassan NZA, Bahari MS, Aminuddin F, Mohd Nor Sham Kunusagaran MSJ, Zaimi NA, Mohd Hanafiah AN, et al.

Front Public Health, 2022;10:959812.
PMID: 36684911 DOI: 10.3389/fpubh.2022.959812

INTRODUCTION: Ambulance services are pivotal in any country's healthcare system. An efficient ambulance service not only decreases patient mortality rate but also allows resource prioritization for better outputs. This study aims to measure the efficiency of ambulance services provided by health facilities in the Ministry of Health (MOH), Malaysia.
METHODS: This cross-sectional study analyzed the efficiency of 76 Decision-Making Units (DMUs) or health facilities, consisting of 62 health clinics and 14 hospitals. Data Envelopment Analysis (DEA) was used for computing efficiency scores while adopting the Variable Return to Scale (VRS) approach. The analysis was based on input orientation. The input was the cost of ambulance services, while the output for this analysis was the distance coverage (in km), the number of patients transferred, and hours of usage (in hours). Subsequent analysis was conducted to test the Overall Technical Efficiency (OTE), the Pure Technical Efficiency (PTE), the Scale Efficiency (SE), and the Return to Scale with the type of health facilities and geographical areas using a Mann-Whitney U-test and a chi-square test.
RESULTS: The mean scores of OTE, PTE, and SE were 0.508 (±0.207), 0.721 (±0.185), and 0.700 (±0.200), respectively. Approximately, 14.47% of the total health facilities were PTE. The results showed a significant difference in OTE and SE between ambulance services in hospitals and health clinics (p < 0.05), but no significant difference in PTE between hospitals and clinics (p>0.05). There was no significant difference in efficiency scores between urban and rural health facilities in terms of ambulance services except for OTE (p < 0.05).
DISCUSSION: The ambulance services provided in healthcare facilities in the MOH Malaysia operate at 72.1% PTE. The difference in OTE between hospitals and health clinics' ambulance services was mainly due to the operating size rather than PTE. This study will be beneficial in providing a guide to the policymakers in improving ambulance services through the readjustment of health resources and improvement in the outputs.

Matched MeSH terms: Efficiency, Organizational*
Impact of environmental regulations on the industrial eco-efficiency in China-based on the strong porter hypothesis and the weak porter hypothesis

Zhong C, Hamzah HZ, Yin J, Wu D, Cao J, Mao X, et al.

Environ Sci Pollut Res Int, 2023 Mar;30(15):44490-44504.
PMID: 36692722 DOI: 10.1007/s11356-023-25410-0

As an important indicator of sustainable development, industrial eco-efficiency (IEE) has aroused growing attention from governments all over the world including China, in recent decades. The Chinese government has introduced numerous environmental regulations; however, the environmental pollution issue does not appear to have been solved. Moreover, although several earlier studies have shown that environmental regulations may promote innovation, there is no consensus on their ultimate effects on IEE. Therefore, this study took a critical look at the connection between environmental regulations and IEE in 36 Chinese sub-sectors from 2009 to 2018. Based on the weak Porter hypothesis (weak PH) and strong Porter hypothesis (strong PH), this paper constructed two panel regression models and conducted group analysis by pollution intensity to check the relationships among environmental regulations, technological innovation, and IEE. It was found that environmental regulations can improve technological innovation and IEE, but these impacts vary across different pollution groups. Specifically, environmental regulations have a U-shaped or inverted U-shaped relationship with technological innovation and IEE. Of the 36 sub-sectors, 26 prove the existence of the Weak PH while 10 verify the Strong PH, indicating that environmental regulations generally advocate technological innovation for most sub-sectors but only promote IEE in a few sub-sectors at present. Finally, differentiated policy implications for environmental regulations and technological innovation are provided for decision-makers.

Matched MeSH terms: Efficiency*
Fulltext Waste heat recovery research - a systematic bibliometric analysis (1991 to 2020)

Kuah CT, Koh QY, Rajoo S, Wong KY

Environ Sci Pollut Res Int, 2023 Jun;30(28):72074-72100.
PMID: 35716302 DOI: 10.1007/s11356-022-21377-6

Human usage of non-renewable energy resources has caused many environmental issues, which include air pollution, global warming, and climate irregularities. To counter these issues, researchers have been seeking after alternative renewable energy sources and ways to manage energy more efficiently. This is where energy recovery technologies such as waste heat recovery (WHR) come into play. WHR is a form of waste to energy conversion. Waste heat can be captured and converted into usable energy instead of dumping it into the environment. In the more recent years, the WHR research field has gained great attention in the scientific community as well as in some energy-intensive industries. This article presents a bibliometric overview of the academic research on WHR over the span of 30 years from 1991 to 2020. A total of 5682 documents from Web of Science (WoS) have been retrieved and analyzed using various bibliometric methods, including performance analysis and network analysis. The analyses were performed on different actors in the field, i.e., funding agencies, journals, authors, organizations, and countries. In addition, several network mappings were done based on co-citation, co-authorship, and co-occurrences of keywords analyses. The research identified the most productive and influential actors in the field, established and emergent research topics, as well as the interrelations and collaboration patterns between different actors. The findings can be a robust roadmap for further research in this field.

Matched MeSH terms: Efficiency
Fulltext The impact of technical efficiency on firms' value: The case of the halal food and beverage industry in selected countries

Rusmita SA, Zulaikha S, Mazlan NS, Mohd Dali NRSB, Cahyono EF, Ramadhani I

PLoS One, 2023;18(11):e0286629.
PMID: 38011115 DOI: 10.1371/journal.pone.0286629

The market for the halal food and beverage industry sector has experienced rapid growth in recent years, which indicate excellent investment opportunities. This paper examine the effect of Technical Efficiency (TE) on firm value in 5 selected influential countries in halal food and beverage sector based on Global Islamic Economy Report 2020. Two steps estimation was used to run the data, using the Stochastic Frontier Analysis (SFA) model to determine the company's TE and panel data to test the effect of TE through firm value. The results show that Indonesia has the highest score for TE (62%), followed by Pakistan (59%), South Africa (57%), Malaysia (55%), and Singapore (52%), which means, in general, there is inefficiency in allocating resources over 38% up to 48% and needs to be improved by halal food and beverage companies in. Regarding panel data, all countries sample except Pakistan highlight that TE significantly affect company value. It indicates that the crucial part of managing efficiency can be a sign in stock market performance. The result shows that company managers should set efficiency strategies to their business process for creating sustainability and increase their value in the capital market. As for investors, this TE can be used as an indicator before choosing company stocks; if the company is efficient, then it is worthy of being one of the portfolio assets. Form the government side, the finding can help them to set appropriate policy setting to boost halal food and beverages industry such as giving subsidy or incentive to increase the efficiency ability of halal food and beverage companies and identify the industry's strength by comparing the result of TE between 5 countries.

Matched MeSH terms: Efficiency