Showing papers in "Socio-economic Planning Sciences in 2021"

Can industrial agglomeration achieve the emission-reduction effect?

Abstract: This paper adopted the Meta-constraints efficiency model to measure the environmental efficiency of the industry in China, and then used the spatial panel model to analyze the impact of industrial agglomeration externalities on environmental efficiency. The study found that industrial agglomeration exerted an apparent spatial spillover effect. Different agglomeration degrees and means may be matched with different environmental effects. With the evolution of agglomeration, the balanced effects among negative externality of scale (pollution effect). Marshallian and Jacobs positive externality (self-purification effect) lead to a U-curved tendency between industrial agglomeration and environmental efficiency. Therefore, with the increase of industrial agglomeration degree, the environmental efficiency first decreases and then increases. The effect of industrial agglomeration in the Midwest on the environment is mainly presented as a negative externality of scale, situated in the descending phase of the U curve. However, the effect of eastern industrial agglomeration on the environment mainly manifested as Marshallian and Jacobs positive externalities and was situated close to the ascending phase of the U curve. All regions should fully utilize the “self-purification” effect of the Marshallian externality and the Jacobs externality on emission-reduction according to the different phases of industrial development.

Disruption in Food Supply Chain and Undernourishment Challenges: An Empirical Study in the Context of Asian Countries

Abstract: Undernourishment and associated health issues are some mammoth challenges that the world currently faces. The poorly design food supply chain (FSC) is considered a root cause of high undernourishment cases worldwide. Since all processes and stages in a supply chain are strongly connected, a slight delay or glitch can trigger a butterfly effect resulting in significant socio-economic losses. The FSC is vital to providing human essentials and a source of bread earning; rank at the top in global industries and any disturbance results in high unemployment and leading social evils like crime and violence in society. Recognize the same; this study examines the impact of food supply chain disruption on undernourished cases in selected Asian countries. Using Generalized Methods of Moments (GMM) estimator, this study provides two key findings. First, a higher intensity of COVID-19 cases translates into higher undernourishment due to direct and indirect effects from higher stringency measures. Secondly, government financial allocations to combat COVID-19 and economic growth significantly mitigate the prevalence of undernourishment. Interestingly, a higher crime index is linked with higher undernourished cases supporting the proposition of socio-economic disorder. These results propose broad policy implications for governments, food regulatory authority, donor agencies, and Non-Governmental Organizations by strengthening the food supply chain and thus reduces undernourishment cases.

An integrated BWM-LBWA-CoCoSo framework for evaluation of healthcare sectors in Eastern Europe

Abstract: In this study, an integrated multi-criteria framework is developed to evaluate a healthcare sector which is one of the main infrastructures for any country. Healthcare sector plays a significant role in economic development and social sustainability of countries. To improve performance of healthcare sectors, it is essentially required to evaluate the healthcare systems based on their specific characteristics in order to resolve their performance related issues based on sustainable development principles under social aspect. For this purpose, the proposed integrated framework applies a novel hybrid weight determination model using best-worst method (BWM) and level based weight assessment (LBWA) to determine the weights of healthcare indicators and subsequently, combined compromise solution (CoCoSo) method is further applied to evaluate healthcare performances of several countries according to the pre-determined indicator weights. To show applicability of the proposed framework, a real time case study for seven countries in Eastern Europe is considered based on the data set of Organisation for Economic Co-operation and Development (OECD). Results show that Lithuania and Slovakia have the best healthcare systems in comparison to countries like Poland and Estonia.

Institutional quality and its spatial spillover effects on energy efficiency

Abstract: Several studies have studied the determinants of energy efficiency However, the influence of government institutions was mostly ignored as only few studies have provided evidence on the role of institutions in enhancing domestic energy efficiency In this paper, we extend the previous literature by asking whether neighboring country's institutions could also have an impact on domestic energy efficiency Thus, with the stochastic frontier approach and spatial econometric model, we investigate the spatial effect of institutional quality on energy efficiency in a panel of 99 countries (economies) for the period 1995–2016 Our results, first confirm the presence of spatial correlations in energy efficiency across countries Secondly, we discover that the direct positive effect of institutional quality on energy efficiency is such that it overcomes the insignificant indirect negative effect, which suggests a total positive and significant effect Our results, therefore, indicate that institutional quality matters in energy efficiency improvement and being close to countries with good institutional framework has a positive effect on domestic energy efficiency Finally, the energy efficiency estimates demonstrate that global energy issue can only be addressed with long-term policies that increases technological progress

Health tourism strategy selection via SWOT analysis and integrated hesitant fuzzy linguistic AHP-MABAC approach

Abstract: Health tourism focuses on the organizational and operational aspects of commercial trips for the treatment of individuals. In line with the economic growth, the industry has evolved significantly in the last few decades. Istanbul, Turkey is considered as one of the most viable markets in the region due to its thermal resources, mild climate, geographical accessibility, and natural resources. This study aims to present the SWOT analysis of Istanbul's health tourism with integrated hesitant fuzzy linguistic (HFL) AHP-HFL MABAC methodology to select the best strategy for its effective implementation. The proposed methodology initially determines SWOT factors required for the analysis. These factors are then weighted with HFL AHP. The results are then utilized to select the best health tourism strategy using HFL MABAC. The applicability of this approach is presented through a case study. This is the first study to propose an analytic based SWOT analysis with integrated HFL methods for the selection of most appealing health tourism strategy.

Does local government competition aggravate haze pollution? A new perspective of factor market distortion

Abstract: Haze pollution has become a new threat to China's sustainable development, but it may be that local government behaviour can play an important role in the prevention and control of pollutants. A dynamic spatial autoregressive (SAR) model is used to study the relationship between local government competition and haze pollution. To further explore the indirect impact of factor market distortion on haze pollution and control potential endogeneity problems, a newly developed intermediary effect model that incorporates the characteristics of the generalized method of moments (GMM) is utilized to explore how factor market distortion indirectly affects haze pollution. The research results show that regional haze pollution in China is characterized by significant spatial correlation, and local government competition has a positive impact on haze pollution; that is, local government competition exacerbates haze pollution. In general, local government competition not only directly leads to an increase in haze pollution but also further intensifies it by distorting the local factor market, and the intermediary role of factor market distortion is approximately 7.04%. The results of the regional inspection found that competition among local governments in the eastern region did not lead to haze pollution, and distortion of the factor market did not exist as an intermediary effect. However, both direct and intermediary effects are significant in the central and western regions. Therefore, an official performance appraisal system that includes ecological constraints should be established to guide the benign transformation of local government competition, and an environmental management mechanism must be developed for joint prevention and control to reduce haze pollution. In addition, the free flow of factors and marketization are equally important.

The puzzle of greenhouse gas footprints of oil abundance

Abstract: The present inquiry lays a groundwork for the analysis of the net greenhouse gas (GHG) footprint of oil in the oil-abundant settings. To address the research question, the study puts forward a three-sector decision model, which provides a common ground for the assessment of the interaction of the structuralist and institutional factors influencing environmental pollution in the oil-reliant economies. The study shows that fossil-fuel abundance triggers forces, which induce diametrically opposed effects concerning atmospheric pollution. These are the rising carbon-intensive oil extraction and processing and fossil-fueled power generation versus shrinkage of the carbon-intensive manufacturing and growth of the low-carbon tertiarization. The theoretical analysis enables compartmentalization of the essential factors, which determine GHG emissions in the respective countries. To assess the significance of the proposed theoretical framework, the study employs multivariate panel co-integration techniques and two-stage fixed effects estimations for a dataset of 38 oil-producing countries for the time period between 1960 and 2018. In contrast to the existing literature, this study drives apart from the black box approaches that employ just one omnibus variable, per capita income.

The socio-economic determinants of COVID-19: A spatial analysis of German county level data.

Abstract: The study explores the association of socioeconomic, demographic, and health-related variables at the regional level with COVID-19 related cases and deaths in Germany during the so-called first wave through mid-June 2020. Multivariate spatial models include the 401 counties in Germany to account for regional interrelations and possible spillover effects. The case and death numbers are, for example, significantly positively associated with early cases from the beginning of the epidemic, the average age, the population density and the share of people employed in elderly care. By contrast, they are significantly negatively associated with the share of schoolchildren and children in day care as well as physician density. In addition, significant spillover effects on the case numbers of neighbouring regions were identified for certain variables, with a different sign than the overall effects, giving rise to further future analyses of the regional mechanisms of action of COVID-19 infection. The results complement the knowledge about COVID-19 infection beyond the clinical risk factors discussed so far by a socio-economic perspective at the ecological level.

Comparison of municipalities considering environmental sustainability via neutrosophic DEMATEL based TOPSIS

Abstract: Considering the increasing risk of various events to the environment, environmental sustainability has taken much more attention both by the academics and practitioners than the other topics. Every organization has its own responsibilities. However, public institutions have a more important role, since they have direct effect on all the community considering environmental sustainability. At this point, the performance evaluation of the municipalities becomes important and enables an effective management by not only indicating the existing status of the municipalities but also revealing the gaps for improvement. Hence, regarding the importance of environmental sustainability and performance evaluation, this study firstly provides the environmental sustainability dimensions with related indicators and presents an integrated methodology based on Neutrosophic Decision Making Trial and Evaluation Laboratory (N-DEMATEL) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for the performance comparison of municipalities. Different from the classical DEMATEL, N-DEMATEL addresses the inherent ambiguity and indeterminacy of decision making process while revealing the importance of factors considering the interaction between them. Afterwards, TOPSIS is used to determine the rank of the municipalities. So as to show the applicability of the proposed methodology, an application is performed in the district municipalities of Istanbul Metropolitan Municipality.

Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters

Abstract: Humans tackle natural disasters all over the world. Humanitarian supply chain plays an important role to mitigate damages occurred after a disaster. This research formulates a multi-objective Mixed-Integer Non-Linear programming (MINLP) with uncertain parameters considering Relief Centers (RC), Demand Points (DP) in affected areas, transportation methods to deliver Relief Items (RI) and different types of RIs namely, perishable and imperishable. In pre-disaster stage, location and number of RCs with their prepositioned inventory level are determined. After disaster strikes, based on a distribution plan the amount of RIs that should be transported to DPs and number of needed vehicles are determined. The objective functions minimize the total distance traveled by RIs, total costs (including RC establish cost, inventory cost, fixed cost for each vehicle type and acquisition cost for RIs), maximum traveling time between RCs and DPs and number of perished items respectively. The proposed model is solved by GAMS software for small size test problems and Grasshopper Optimization Algorithm (GOA) as a meta-heuristic approach for large size problems. Numerical and computational results are provided to prove the efficiency and feasibility of the presented model. Finally, the developed model is implemented to Iran's flood in 2019 as a case study.

Consumer behavior during the COVID-19 pandemic: An analysis of food purchasing and management behaviors in U.S. households through the lens of food system resilience

Abstract: The COVID-19 pandemic has stimulated considerable interest in the resilience of the U.S. food system. Less attention has been paid to the resiliency characteristics of the final link in the food system – individual households. We use national survey data from July 2020 to understand the food acquisition, preparation, and management strategies that households implemented in response to the pandemic. We find a substantial increase in the amount of food prepared and consumed at home which scales with respondents’ time availability, perceived risks of dining out, and pandemic-induced income disruption. We then identify several household responses to support this increase in home food consumption that are in line with practices suggested to enhance resiliency at other links in the food supply chain, including increased cold storage capacity and enhanced in-house capability via improved cooking and food management skills. We discuss how responses such as improved food skills can reduce the propagation of shocks through the supply chain by allowing greater flexibility and less waste, while actions such as increased home cold storage capacity could undermine system resilience by exacerbating bullwhip effects, i.e., amplifying consumer demand shocks that are propagated to upstream food supply chain actors.

The impact of internet development on the health of Chinese residents: Transmission mechanisms and empirical tests

Abstract: The development of internet information technology has promoted the integration of modern information technology with medical treatment. Chinese interprovincial panel data from 2006 to 2017 are used to study the impact of internet development on the health of Chinese residents. Specifically, this paper builds a system for evaluating four dimensions of internet development (internet popularity, internet infrastructure, internet information resources, and internet applications) and uses the entropy method to calculate and analyze China's internet development level. The OLS, FGLS and IV models are used to analyze the impact of internet development on the health of Chinese residents. The regional heterogeneity of the impact of internet development on the health of Chinese residents is studied in the three major regions, i.e., eastern, central and western China. The transmission mechanisms through which internet development affects residents' health were also investigated. Study results show that the development of the Internet significantly suppresses the mortality rate and improves residents' health. In addition, the development of the Internet can further reduce the mortality rate by promoting the development of science and technology, education, medicine, urbanization and openness.

The effect of artificial intelligence on carbon intensity: Evidence from China's industrial sector

Abstract: Artificial Intelligence (AI) is becoming the engine of a new round of technological revolution and industrial transformation; as such, it has attracted much attention of scholars in recent years. Surprisingly, scarce studies have shed lights on the effects of AI on the environment, especially with respect to carbon intensity. Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we use Chinese industrial sector data from 2005 to 2016 to investigate how AI affects carbon intensity. The empirical results show that AI, as measured separately by the adoption of robotics by industry and the number of academic AI-related papers, significantly reduces carbon intensity. The results remain robust after addressing endogenous issues. We find that there are both stages and industrial heterogeneity in the effects of AI on carbon intensity. AI had a more decrease effect on carbon intensity during the 12th Five-Year Plan than the 11th. Compared with capital-intensive industries, AI tends to have a more decrease effect on carbon intensity in the labor-intensive and tech-intensive industries. To enlarge the effects of AI on reducing carbon intensity, the government should promote the development and application of AI and implement differentiated policies in line with the industry characteristics.

Assessment of the sustainable development of rural minority settlements based on multidimensional data and geographical detector method: A case study in Dehong, China

Abstract: In a developing country, paying attention to the sustainable development of rural areas is conducive to the development of the entire country. Ethnic minority areas are an important part of China's economic and social development. Owing to a lack of relevant statistical data, most previous studies in this area have focused on the sustainable development of rural areas or the development of ethnic minorities, but have not studied the sustainable development of rural ethnic minorities. The development of rural ethnic minorities is worthy of attention. In this study, we took Dehong as the study area. First used toponyms to accurately identify the rural minority areas and then calculated a grid of settlement density. Second, we considered the digital number (DN) value of the visible infrared imaging radiometer suite (VIIRS) as a measure of the development of the region and digital elevation model (DEM), net primary productivity (NPP), normalized difference vegetation index (NDVI), and gross domestic product (GDP) data as the indicators of terrain, climate, ecological, and economic factors, respectively. Finally, linear regression and the geographical detector method were used to determine the weight of the factors for constructing a sustainable development index (SDI) to quantitatively analyze the sustainable development and influencing factors of each minority nationality. The factors evaluated using linear regression and the geographical detector method were ranked as follows: NDVI > elevation > GDP > slope > NPP > settlement density. The results demonstrate that of the five main ethnic minorities in Dehong, Dai and Jingpo have higher SDI, followed by Achang, Lisu and De'ang. In addition, we provide some suggestions for ethnic minorities in Dehong.

The performance of green innovation: From an efficiency perspective

Abstract: In this new stage of global economic development, we hope to achieve both economic development and environmental improvements via innovation. Green innovation aims to meet the dual goals of economic development and ecological protection. The scientific evaluation of the performance of China's green innovation appears to be quite meaningful. Some studies have evaluated the performance of green innovation, but are limited by the use of a single efficiency measurement method. To fill this research gap, this article uses a combination of two methods to evaluate green innovation efficiency, which provides a more precise evaluation of efficiency. Specifically, this article uses the vertical-and-horizontal scatter degree method to construct a pollutant index and then sets that index as the undesirable output in a slacks-based measure (SBM) model to evaluate efficiency. To further study the regional differences in green innovation efficiency, this article uses a convergence model. Most existing convergence analyses ignore spatial elements. Focusing on the influence of spatial factors, this article introduces a spatial econometric model into the convergence analyses. This article draws the following main conclusions. (i) The efficiency of green innovation in the country as a whole has been increasing each year, and green innovation efficiency in the central and western regions has increased significantly. (ii) Regional differences have narrowed each year. (iii) Green innovation efficiency is significantly positively spatially correlated, which is reflected in the spatial agglomeration of regions with the same efficiency level. (iv) Green innovation efficiency exhibits σ-convergence and spatial conditional β-convergence. (iv) Spatial factors accelerate the convergence of green innovation efficiency.

The spatial impacts of air pollution and socio-economic status on public health: Empirical evidence from China

Abstract: The impact of air pollution or socio-economic status on public health is a lively topic for social economists and environmental researchers across the world. Despite a lot of research, there is still a lack of clear understanding of the spatial heterogeneous impacts related to public health. In this study, we use the spatial econometric model with panel data to investigate the direct and interactive effects of air pollution and socio-economic status on public health and further explore the regional heterogeneity among different regions. The research results show that there is strong spatial agglomeration in air pollution, socio-economic status, and public health. Aggravation of air pollution significantly damages local public health status, leading to increased infant mortality rate and lower average life expectancy. Aggravated air pollution in one province significantly increases infant mortality in neighboring provinces. The increase in per capita income significantly leads to a significant positive effect on public health. As air pollution continues to increase, the impact of per capita income on improving public health has diminished gradually. Improving the level of education per capita significantly improves public health. As per capita education level increases, the impact of increased air pollution on public health damage has diminished gradually. The effects of air pollution and socio-economic status on infant mortality in the eastern, central, and western regions are heterogeneous.

Sustainability Performance Analysis of Small and Medium Sized Enterprises: Criteria, Methods and Framework

Abstract: Small and medium sized enterprises (SMEs) play an important role in any economy as they contribute to GDP and employment. However, sustainability (right combination of economic, environmental and social) of SMEs is a major concern as they prioritize economic performance over environmental and social to remain competitive. Majority of prior researches on SMEs' sustainability either look at the impact of a few limited enablers (e.g. lean, green, innovation etc.) on sustainability performance or the effect of pressures and barriers on the sustainability performance. There is a clear gap of a holistic and robust framework for sustainability performance analysis in order to measure and improve sustainability performance. This research bridges this knowledge gap by addressing two research questions – what practice and performance criteria are being considered for sustainability performance analysis in a broad environmental, economic and social context, how are they related, and what methods are being used to derive the relationship between sustainability practices and performance. These research questions are addressed through a systematic literature review of 58 papers, published between 2005 and 2018 in leading journals. First, an objective content analysis is undertaken in order to identify sustainability practices and performance criteria along with their frequency of usage in prior research. Second, the correlation among the variables is studied. Third, the methods for analyzing the relationships of the criteria are identified. Finally, a framework for analysing correlation of SMEs’ sustainability practices and performance in order to measure and improve performance using statistical modeling approach is proposed.

Measuring consumers’ level of satisfaction for online food shopping during COVID-19 in Italy using POSETs

Abstract: The pandemic COVID 19 has upset the economic, social, financial, and general behavioral systems. Global crisis has a large impact overall and related fallouts significantly affect existent structural paradigms in every country and region across the world. In particular, the spread of COVID-19 pandemic has led to having to rethink the way we produce and consume food. Within this global change, a rise in the number of consumers who purchase food products online in order to comply with the rules aimed at limiting the circulation of the virus should be emphasized. Consequently, probably causing a long-term positive effect on m-commerce. The purpose is to elaborate on the index of the satisfaction level of consumers of purchasing food online via food shopping channels, by using key factors that characterize the online spending behavior. The analysis was carried out by collection of data deriving from an anonymous online questionnaire administrated via social networks and emails, during the ‘hot’ months of the pandemic progression in Italy, which is March–May 2020. We analyse both dimensions of customer satisfaction (process and outcome), by means of two systems of indicators. We reduce their complexity using synthesis obtained with the Partially ordered set. Results highlight the differences between the two dimensions of customer satisfaction. Online shopping can surely contribute to reduction of food waste thanks to elimination of frenzied shopping routines at supermarkets and can open space to new fields of study. On the other hand, defining an index of the consumer's satisfaction can alter sales strategies of m-commerce managers and entrepreneurs.

Dynamically evaluating technological innovation efficiency of high-tech industry in China: Provincial, regional and industrial perspective

Abstract: This study firstly adopts Data Envelopment Analysis (DEA) window analysis with an ideal window width to dynamically investigate the technological innovation efficiency of China's high-tech industry during 2009–2016, simultaneously from provincial, regional and industrial perspective. The ideal window widths in the high-tech industry and its five sub-industries are all 4. The findings indicate that the efficiency of high-tech industry is low and presents a wave-shaped trend, as well as presents large inter-provincial and inter-regional differences. The efficiency in eastern region is always the highest, while the efficiency in northeastern region is the lowest. Moreover, the efficiencies in eastern region and western region both presented wave-shaped decrease trends, while the efficiencies in central region and northeastern region both presented wave-shaped increase trends. There are significant inter-regional and inter-provincial differences in efficiency of each sub-industry. The distributions of efficiencies of various provinces in five sub-industries are different. No province has always been on the innovation frontier for the entire evaluation period. The province with larger number of years on the frontier generally has the higher efficiency score, although there are some exceptions. Among the provinces on the frontier in various industries, the eastern provinces account for a large proportion.

Public-private partnerships in health care services: Do they outperform public hospitals regarding quality and access? Evidence from Portugal

Abstract: Public-private partnerships (PPPs) are widely spread long-term arrangements between governments and strategic private partner(s). One of their objectives is to reduce the financial pressure on the public treasury with regard to new investments. PPPs have been employed within the health care sector which, in turn, carries a huge social burden. In Portugal, for instance, PPPs in health care concern bundling hospital infrastructure and clinical services management. Notwithstanding the need to ensure sustainability and efficient use of hospital resources, it is clearly compulsory to guarantee that patients receive appropriate and timely care, with maximum security, and equitable manner. Still, little or even no attention has been paid in the literature to the clinical response capacity of PPP hospitals and to the populism arguing that these entities have a lower social performance than typical public hospitals. This study uses robust benchmarking methodologies alongside recent data about Portuguese hospitals (FY2012-FY2017) to demystify this idea and to demonstrate that, actually, PPP hospitals can deliver health care services with social performance levels at least as good as public hospitals.

The Covid-19 pandemic and food consumption at home and away: An exploratory study of English households

Abstract: The Covid-19 pandemic has changed attitudes of English households towards food consumption at home and when eating out. Little academic research has however examined the scope and the scale of these changes, especially in the context of foodservice provision. This mixed methods study explores the effect of Covid-19 on food consumption in English households at home and away. It reveals increased frequency and variety of cooking during lockdown as a driver of household food wastage. The study demonstrates public hesitance towards eating out post-Covid-19. Foodservice providers are expected to re-design their business settings and adopt protective and preventative measures, such as frequent cleaning and routine health checks, to encourage visitation. After the pandemic, increased preference towards consuming (more) sustainable food at home, but not when eating out, is established. These insights can aid grocery and foodservice providers in offering more tailored products and services in a post-pandemic future.

Cluster Analysis for mixed data: An application to credit risk evaluation

Abstract: Credit risk is one of the main risks faced by a bank to provide financial products and services to clients. To evaluate the financial performance of clients, several scoring methodologies have been proposed, which are based mostly on quantitative indicators. This paper highlights the relevance of both quantitative and qualitative features of applicants and proposes a new methodology based on mixed data clustering techniques. Indeed, cluster analysis may prove particularly useful in the estimation of credit risk. Traditionally, clustering concentrates only on quantitative or qualitative data at a time; however, since credit applicants are characterized by mixed personal features, a cluster analysis specific for mixed data can lead to discover particularly informative patterns, estimating the risk associated with credit granting.

An integrated CRITIC and MABAC based type-2 neutrosophic model for public transportation pricing system selection

Abstract: The pricing of public transportation services is a complex task that authorities deal with because many criteria should be considered while deciding on the pricing system. Some include decentralization of the cities due to lower rents in outer-city regions and operating costs of longer transportation lines. Hence, four alternative public transportation pricing systems are defined, which are flat fare, distance-based, zonal, and rent-based fare. To prioritize these alternatives, four aspects are determined, namely cost, transportation, social and political, and there are 13 criteria present under these aspects. A two-stage hybrid multi-criteria decision-making model based on type-2 neutrosophic numbers (T2NNs) is introduced to provide a straightforward and flexible decision-making tool for researchers and practitioners. In its first stage, the reputation of the experts is determined under the T2NN environment. Second, the novel T2NN-based CRiteria Importance Through Intercriteria Correlation (CRITIC) method is employed to evaluate the criteria importance, while the new T2NN-based Multi-Attributive Border Approximation area Comparison (MABAC) method is used to rank the public transportation pricing systems. The results show that rent-based fare pricing is the most advantageous alternative. The high reliability and robustness of the integrated CRITIC and MABAC based type-2 neutrosophic model are demonstrated with the comparative and sensitivity analyses.

Best-Worst PROMETHEE method for evaluating school performance in the OECD's PISA project

Abstract: This paper introduces the Best-Worst PROMETHEE method, which avoids the rank reversal problem of other PROMETHEE methods. We use this new technique to rank schools in the Programme for International Student Assessment (PISA). We consider three separate outputs, provided by average student attainments in mathematics, language, and sciences. Our sample comprises 16,500 schools in 66 countries, and our results show significant differences between school performance both within and between countries. The top half of the ranking mainly comprises European and Asian countries, while the bottom half includes many North African, Middle Eastern, and South American countries. We find no strong association between inequality between schools and the country-level performance, suggesting the absence of a trade-off between equity and performance in education. The Best-Worst PROMETHEE is a generic method that can be used to support decisions in strategic sectors with multidimensional outcomes.

Environmental sustainability and service quality beyond economic and financial indicators: A performance evaluation of Italian water utilities

Abstract: As water utilities operate as natural monopolists and they provide essential services for human life, their activities are regulated by public authorities. The sustainable use of water resources and a specific attention on social needs should be essential goals for this kind of firms, so that the evaluation of their business should go beyond their profitability and their financial solvency. Keeping pace with the new Circular Economy paradigm and the evolution of the water regulatory framework, in this paper we suggest a global composite indicator apt to evaluate in a novel way the water utilities performance, encompassing financial and economic indexes together with environmental sustainability and service quality measures. To show its empirical implementation we evaluate the performance of Italian water utilities. The operating context is also under scrutiny focusing on specific water utility features such as size, geographical location, degree of diversification and ownership. In this light, operating in the Centre and being large are considered favourable background conditions, while the South and the medium size display a significant unfavourable influence on the water utility performance. Multi-utilities are more advantaged with respect to the mono-utilities and no significant distinction can be made among the different ownership models.

Supplier selection in the oil & gas industry: A comprehensive approach for Multi-Criteria Decision Analysis

Abstract: The focus of this paper is on selecting suppliers in the Oil and Gas (O&G) industry by developing a comprehensive approach for Multi-Criteria Decision Analysis (MCDA) based on alternative methods capable of assessing different aspects of supplier selection uncertainty in terms of utility functions and criteria related to efficiency. The O&G industry has a key role in the public sector of various countries such as Iran with its revenues being of prime importance to develop infrastructure facilities such as for healthcare, education, and transportation. This comprehensive approach walks through various stages for selecting Critical Success Factors (CSFs), ranking suppliers, and for setting partial weighting alternatives. While CSFs are selected using a traditional Delphi approach, the partial supplier rankings are defined based on Complex Proportional Assessment (COPRAS) utility functions together with criteria weights derived from Step-wise Weight Assessment Ratio Analysis (SWARA) for each CSF. As it concerns information reliability of utility and efficiency functions of both methods obtained via expert preferences or perceptions, Z-numbers are used to address the intrinsic fuzziness level inherent to each analytical stage. Iran's economy depends on revenues from oil and other related production, which means that by earning more income from this industry, most of its economic indicators such as GDP and employment rate should increase significantly, thus leading to economic growth. Various countries put plans in place related to production for increasing their social economics. One of these plans is focused on suppliers since they have a high impact on providing essential items such as equipment, HR, and transportation, so by choosing the best suppliers in all fields, costs will decrease and consequently revenue will increase. This research points out how to rank O&G industry suppliers using MCDA methods in an uncertain environment. An example based on actual data from an Iranian O&G company is provided to show the applicability of the approach proposed. Results suggest that the complexity of O&G operations on selecting suppliers can be adequately handled by information reliability techniques applied to traditional economic concepts such as utility- and efficiency-related factors, particularly in business environments characterized by a trade embargo.

The path to renewable energy consumption in the European Union through drivers and barriers: A panel vector autoregressive approach

Abstract: Renewable energy consumption brings sustainable economic growth and pollution reduction. Despite the worldwide increase in renewable energy consumption, global energy-related carbon dioxide emissions are rising and there are still considerable differences in the share of renewable energy consumption in national energy portfolios. These concerns require further effort at the policy level, especially by countries that make extensive use of energy imports. These countries could improve their lack of energy independence by using renewable energy sources and leveraging a few factors to facilitate their transition. This study aims to investigate renewable energy consumption drivers, focusing on the role of socio-technical (rather than economic) aspects such as policy stringency, lobbying, public awareness, and education. We employ a panel vector autoregressive model in first differences to test the complex dynamic relationships among renewable energy consumption, policy stringency, lobbying, public awareness, and education, controlling for variables such as per capita income and import levels, for 12 European Union net energy importing countries. Results show that the positive income effect prevails in the influence of the level of carbon dioxide emissions (negative) on renewable energy consumption, despite the latter being more significant in countries with higher levels of education. Increasing energy needs push traditional sources towards complementarity with renewable energy consumption, implying a positive lobbying effect. Public awareness is not enough to facilitate the transition to renewable energy consumption. By contrast, policy stringency has positive direct and indirect effects on renewable energy consumption, suggesting that the approach adopted by the European Commission in the recent Green Deal is a step in the right direction. Moreover, as shown, policymakers are able, through renewable energy consumption, to generate a decrease in carbon dioxide emissions and electricity production from oil, gas, coal, and nuclear sources in the first instance, but also in net energy imports, even if at a later stage.

Use of DEMATEL and COPRAS method to select best alternative fuel for control of impact of greenhouse gas emissions

Abstract: Generation of energy is a vital process for sustenance of human life. Quality of human life is undoubtedly linked to the efficient generation and use of energy. The choice of alternative fuels is of the utmost significance due to the decline of fossil fuel reserves and their effect on global warming. One of the most important areas of research all over the world is the generation and distribution of sustainable energy. There are, in fact, many sustainable fuel resources. In this study, we describe the problem of selecting alternate fuel using novel types of hesitant multi criteria decision-making equation methods. The considered fuel systems are Electricity, natural gas, biodiesel, ethanol, and propane. In the selection of alternative fuels, quantity, quality of performance, cost, and efficiency among others, have to be taken into account. The alternatives selected should, for example, increase the speed of buses, and provide for greater mileage while and not affecting the environment. Here, the DEMATEL (Decision Making Trial and Evaluation Laboratory Model) method is used to determine the weights of the criteria and the COPRAS (Complex Proportional Assessment) method is used to calculate the ranking of the alternatives. The main objective of this research paper is to select the best alternative, based on environmental safety, C O 2 emission level, technical cost, and fuel cost. Thus a better alternative is selected with the selected alternatives and criteria. The results of this research are summarized as follows. These are natural gas ( R 2 ) > propane ( R 5 ) > biodiesel ( R 3 ) > electricity ( R 1 ) > ethanol ( R 4 ). The numeric values of these selected alternatives are R 2 = 1 > R 5 = 0.5215 > R 3 = 0.4904 > R 1 = 0.4887 > R 4 = 0.3299 .

COVID-19 induced lockdown effects on agricultural commodity prices and consumer behaviour in India – Implications for food loss and waste management

Abstract: Distortion in distribution and consumption of agricultural commodities is a result of disruptive shocks in prices and food value chains leading to a significant food loss as well as waste. We investigated the COVID-19 induced lockdown effects in India through an interrupted time series analysis coupled with a survey result of 729 consumers, 225 farmers and synthesis of the literature evidence on food loss as well as food waste. Our article complements the literature inventory on COVID-19 by estimating and tracking the effects on prices and consumer behaviour apart from discussing the implications for food loss and waste. Prices post-lockdown shot up immediately and significantly for chickpea (4.8%), mung bean (5.2%), and tomato (78.2%) corroborating the loss in highly perishable product – tomato – owing to its spiked price. We find no structural break in prices due to lockdown implying that lockdown-induced price change was not sufficient to alter the long-run price movement, and the prices of the major commodities reverted to the pre-lockdown levels. The pandemic induced lockdown did restrict the access to food markets and a majority of consumers (75.31%) experienced a price increase across COVID zones of different intensity of incidence leading to food loss along supply chain and wastage at consumers end. Consumers’ livelihood affected from moderate (59.53%) to severe (3.3%) with 92 per cent reporting a change in shopping behavior. The Kruskal-Wallis test on consumption behavior change indicated a significant shift among the consumers reporting altered income, mostly in the downside, post-lockdown. Despite the relaxation for agricultural related activities during the lockdown, farmers reported disruption in disposing their winter produce barring wheat, bolstered by a record state procurement in 2020. The paper affirms that the pandemic has caused a significant price change and unprecedented panic purchase that led to the food wastage but subsided soon exhibiting the resilience in Indian agriculture. We strongly recommend for promoting the capacity and collective resilience of small-scale production systems through institutions, policies and reforms. Contract farming, farmer producer organizations, creation and functioning of social safety nets to overcome income, production and price shocks, access to digital national markets and capacity building on food waste management practices will insulate vulnerable section as well as reduce the loss of food across supply chain.

Socio-economic evaluation model for sustainable solar PV panels using a novel integrated MCDM methodology: A case in Turkey

Abstract: Due to the increasing awareness of environmental, social and economic factors, solar photovoltaic (PV) system planning requires strategic decision making process for socio-economic development in many countries. The main objective of this paper is to propose a new Multi-Criteria Decision Making (MCDM) approach that is flexible and practical to the decision makers (DMs) in governments for solar PV panel manufacturer evaluation based on qualitative and quantitative factors. Accordingly, a novel two-stage MCDM model integrating Analytic Hierarchy Process (AHP) and Multiplicative Multi-Objective Ratio Analysis (MULTIMOORA) methods under Interval Valued Pythagorean Fuzzy (IVPF) environment is presented, and applied to select the most appropriate solar PV panel manufacturer for solar power plants in Southeastern Anatolia Region of Turkey. As a result, the proposed novel Integrated IVPF-AHP&MULTIMOORA method produces consistent and reasonable results to select the most appropriate solar PV panel manufacturer for the solar power plants in the cities of Southeastern Anatolia Region of Turkey considering some socio-economic sustainable indicators such as cost, environmental, efficiency and technical indices. Furthermore, sensitivity analysis and comparative analysis are also applied to prove the robustness and verification of the results of the proposed approach.