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The booming development of customised e-commerce makes e-commerce supply chains experience a severe test of consumer service level (CSL). An efficient e-commerce supply chain resilience optimisation method addressing economic performance and service performance is proposed to improve customised services under disruption risks. The proposed method includes a hybrid strategy considering both a resistance strategy with blockchain (BCT) adoption and time-dependent recovery strategies simultaneously. A two-stage multi-period multi-product stochastic programming with BCT adoption is then proposed, which (1) implements BCT as the resistance strategy in the pre-disruption stage; (2) collaborates four recovery strategies in the post-disruption stage; (3) supports the e-tailer in making decisions and optimises his profit and order fulfillment time while taking product priorities into account. Using the actual data of Chinese e-commerce during the public health emergency in 2020, it is demonstrated that (1) the applicability of the model with BCT adoption in both supply chain resilience and CSL improvement; (2) the performance of the hybrid strategy in long-term disruption management; (3) the efficiency and robustness of the proposed solution approach for multi-objective high-dimensional stochastic programming problems. E-tailers can optimise decision-making under long-term disruptions by the proposed methodology in practices. [ABSTRACT FROM AUTHOR]

This paper investigates the association between life insurers' voluntary disclosure of embedded value (EV), an unregulated market-driven fair value measure, and analyst forecast accuracy and dispersion. EV is an estimate of the present value of future net cash flows from in-force life insurance business. Advocates of this disclosure believe that EV is a better measure of economic performance than traditional GAAP measures. Others argue that corporate management has discretion in estimating and reporting EV. Further, analysts may have access to information that allows the development of possibly more accurate estimation metrics in the absence of EV disclosure. It is then an empirical issue to determine whether EV disclosure has any incremental effect on analysts' forecast properties. Using a multi-country setting, we find that EV disclosure is positively associated with analysts' earnings forecast accuracy and negatively related to forecast dispersion. This result is consistent with the alternative hypothesis that disclosure of EV provides a richer information set that enriches analysts' forecasts beyond their own in-house developed surrogates. As guidance for insurance accounting and disclosure evolves, our findings support the value of continuing to provide EV information to the public. [ABSTRACT FROM AUTHOR]

The effective operations of closed-loop supply chains (CLSCs) can help companies achieve sustainability goals and boost economic performance. In practice, CLSCs must collect used products, a complex process that is often constrained by collection station capacity. However, how collection station capacity influences the bullwhip effect and the dynamic performance of CLSCs remains unclear. Here, we develop a system dynamics model for CLSCs that integrates traditional manufacturing with remanufacturing and explore the effects of the stochastic capacity constraint of the collection station on the bullwhip effect of CLSCs. We find that, generally speaking, a collection station with looser or more stable capacity constraints tends to reduce bullwhip of CLSCs. However, pertinent interactions emerge between the relevant parameters; in some situations, reducing the capacity level of a collection station may be reasonable and beneficial when the stochastic capacity constraint is very stable, or when customer demand is highly variable. We also consider the partial backlog in return collection, a phenomenon associated with the stochastic capacity constraint of a collection station, and identify a new trade-off between CLSC sustainability and economic performance. Ultimately, our findings provide evidence that will guide managers' plans for the capacity management of return collection in CLSCs. [ABSTRACT FROM AUTHOR]

In practice, without clear evidence of the positive impacts of maintenance on sustainability, organisations/ companies are not encouraged to look at (or invest in) maintenance as an effective tool to enhance sustainability. Therefore, it is essential to show them that such evidence exists, which is the aim of this paper. The paper reviews and analyses evidence from the literature about maintenance's social, environmental, and economic impacts. It identifies the required sustainability-related indicators associated with these impacts and provides aggregate quantified percentages for them (positively or negatively), as shown in the reviewed papers. An evidence-based research is conducted in this paper to achieve this purpose. The search process results in a research sample of 58 publications that have been surveyed and analysed. Based on the conducted analysis, the results show that maintenance positively impacts economic, environmental, and social sustainability. Many reviewed cases appear in manufacturing and buildings (both residential and commercial) and primarily consider environmental and economic sustainability indicators. The cases with social sustainability indicators are limited. Regardless, these results bring adequate evidence to encourage researchers and practitioners to view maintenance as a practical approach to improve sustainability and to investigate more in this domain. [ABSTRACT FROM AUTHOR]

AbstractObjectiveMethodsResultsConclusionSocioeconomic status deprivation is known to be associated with self-harm in Western countries but there is less information about this association in Low and Middle Income Countries (LMIC). One way of investigating this is to assess the prevalence of indicators of financial stress in people who self-harm. We have assessed the prevalence and correlates of day-to-day financial hardships amongst individual presenting with non-fatal self-harm to hospitals in Sri Lanka.Data on non-fatal self-harm presentations were collected from an ongoing surveillance project in 52 hospitals in Sri Lanka. A questionnaire captured data on two forms of financial stress: unmet need (i.e., costs and bills that cannot be paid) and required support (i.e., steps taken to cover costs, such as selling belongings). Additional data on demographic, economic and clinical characteristics were also collected.The sample included 2516 individuals. Both forms of financial stress were very common, with pawning/selling items (47%) and asking family or friends for money (46%) in order to pay bills or cover costs being commonly reported. Greater financial stress was associated with being aged 26-55 years, limited education, and low socioeconomic position. Financial stress was greater in women than men after adjusting for other factors.The results indicate that financial stress is commonly reported amongst individuals presenting to hospital with non-fatal self-harm in Sri Lanka, especially women. The research highlights a need to attend to financial stress both within self-harm prevention and aftercare. [ABSTRACT FROM AUTHOR]

China's manufacturing sector has fallen into recession since 2010 and one key bottleneck is the increase in operating cost of enterprises. To step out of the predicament, it is critical to reduce cost. By collecting data of 29 manufacturing industries in China during 1980–2020, this paper jointly measures translog cost function and factor input share function and detects the effects of infrastructure investment on cost structure and performance of China's manufacturing industries. It finds infrastructure investment reduces enterprises' operating cost and their demand on labour force. The rate of return to infrastructure is greater than that to private capital, indicating that insufficient supply of infrastructure holds back the development of manufacturing industries. Therefore, it is highly advocated that Chinese government should increase investment in infrastructure construction. [ABSTRACT FROM AUTHOR]

Introduction: When a distributed photovoltaic (PV) system has access to a large urban distribution network, the active balance is primarily borne by the main network gas unit; when the scale of the distributed PV system is very large, the main network can only provide limited regulation capacity, and the distribution network must determine the active optimal scheduling strategy. Methods: This work proposes an active optimization scheduling model for the distribution network by considering the regulation capacity of the main network. In terms of the optimisation objectives, the maximum consumption of the distributed PVs and minimum power fluctuation at the demarcation point of the main distribution network are proposed as the main objectives, while the minimum total exchanged power in a cycle at the main distribution demarcation point and minimum distribution network loss are considered as the secondary objectives. In terms of constraints, it is proposed that the main network's regulation capacity be characterized by the main network's gas-fired unit creep constraints. A fast solution method for active optimization of the distribution network is designed herein to formulate the priority control order of the adjustable units according to the dispatch economic performances of various types of adjustable resources in the distribution network; this reduces the number of variables involved in the optimization at each step and improves the optimized solution speed. Results: Finally, Simulation verification by IEEE 33-node distribution network arithmetic example based on Matlab simulation platform. Discussion: Simulation results show the effectiveness of the method in achieving maximum PV consumption and reflecting the limited regulation capacity of the main grid. [ABSTRACT FROM AUTHOR]

1. This study compared two broiler housing models with different technologies (conventional versus fully automated climate control) to verify their performance and carcass characteristics at slaughter, as well as the economics of production.2. A database regarding 20 443 flocks of heavy broilers produced in an integrated operation during the years 2020 and 2021, in eight Brazilian regions was used in the analysis. The dependent variables included feed conversion, average live weight gain, total and partial carcass condemnation and the total mortality. For economic analysis, the production cost of each technology including feed, labour, energy, heating and depreciation was calculated.3. The technology used in the broiler houses had a significant effect on the technical indicators which were positive for fully automated climate control in most geographical regions. One important exception was the effect on total and partial carcass condemnations, with better results seen for conventional housing. The total cost per ton of broiler meat delivered, deducting condemnation losses, was lower in the automated climate controlled housing compared to conventional housing.4. In conclusion, there was a reduction in broiler chicken production costs for birds raised in fully automated climate controlled housing, indicating better economic results for this model in Brazil. [ABSTRACT FROM AUTHOR]

Purpose: The construction industry has been observed to be one of the most dangerous sectors in which to work; thus, it has struggled to attract new employees and suffered from skill shortages. Another characteristic is adopting a reactive approach to preventing accidents instead of a proactive approach that implements safety-leading indicators. In contributing to achieving a safer construction industry, this study investigated the barriers to achieving a proactive approach to safety in the construction industry. Method: The study adopted a quantitative approach. Data was collected by randomly administering a well-structured questionnaire to industry professionals in South Africa. One hundred and two questionnaires were collected and used as the basis for data analysis and interpretation by the study. Result: The analysis of the collected data identified nineteen significant barriers which were clustered into two based on an exploratory factor analysis. The top three ranked significant barriers are Lack of Top management's commitment to safety, Lack of leadership training and Prioritising productivity over employee safety. The two clusters formed from the nineteen barriers through factor analysis are top management approaches to safety and Safety culture through employee-centric training and accountability. Originality: Considering the accident data from South Africa, and the inherent benefits of implementing SLI to overcome this, this study provides critical insights for industry stakeholders and policymakers. It fills a gap that has not been filled in the developing country context before now. [ABSTRACT FROM AUTHOR]

Purpose: In 2015, assisted reproductive technology (ART) accounted for 1.7% of all U.S. births, donor eggs accounted for over 17,000 started cycles in 2015, and donor sperm accounting for 6.2% of all cycles started in 2014. With increasing utilization of donor gametes as a method of assisting patients with infertility, the number of babies born each year utilizing gamete donation will also continue to increase. This study aimed to elucidate factors impacting decision to donate, amongst a representative national population. Methods: A survey was distributed via the internet utilizing SurveyMonkey Enterprise with HIPAA compliance. Univariate regressions and frequencies were conducted between each demographic and personal characteristic and the willingness to donate. Log Binomial and linear regression was used categorical and continuous variables, and Risk ratios were calculated. Results: In this large survey study, 64% of men and 50% of women reported they would be willing to donate gametes, with the majority desiring monetary compensation. Men with a high Consumer Financial Protection Bureau score were less likely to report that they would consider donating sperm compared to a medium high CFPB score. No other financial indicators were associated with considering donating sperm. There were no associations between CFPB score and egg donation outcomes. Black or African American women were less likely to consider donating their eggs compared to other groups, and more likely to desire > $5000 in compensation. Conclusions: In this large survey study, a small minority of participants reported they would be willing to donate to an unknown infertility patient for reproductive purposes. High and very high CFPB scores were associated with willingness to donate games, but not with desire for monetary compensation or amount. [ABSTRACT FROM AUTHOR]

Context: The reduced utilisation of inputs in milk production holds significant importance from both sustainability and profitability perspectives. Aim: This study examines the efficiencies of each input separately utilised in specialised milk farms across the European Union (EU) countries and explores the factors that influence input efficiencies by using non-radial data envelopment analysis (DEA) model with Russell measure method. Methods: Data from the dairy report of the EU milk-specialised farms compiled by the European Commission (EC) and the Farm Accountancy Data Network (FADN) covering the period from 2013 to 2020 were analysed. The study employed a DEA model to assess efficiency, with a focus on energy, labour, dairy cows, homegrown feed, purchased feed, and operating costs. Key results: Ireland, Italy, Malta, and the Netherlands demonstrated full efficiency across various aspects of milk production, highlighting their exemplary performance in resource utilisation within the EU. Furthermore, the study identified significant potential for efficiency improvements across the EU countries, with potential reductions ranging from 48.8% to 65.616% in various input usage categories. Conclusions: The analysis underscored the importance of improving input efficiency to enhance competitiveness in the dairy sector. Implications: As milk prices and economic performance improve, specialised milk farms tend to become more efficient in managing operating expenses. However, expanding the land area for forage may lead to diminishing returns in resource efficiency, indicating complex interactions and trade-offs affecting input efficiency. Understanding input efficiency in specialised milk farming is crucial for enhancing sustainability and productivity in the European Union (EU) dairy sector. This study addressed the problem of inefficient resource utilisation in EU milk farming, discovering that countries such as Ireland and the Netherlands achieve full efficiency, whereas others have significant potential for improvement. The findings indicated that improving resource efficiency can lead to significant cost savings, environmental benefits, and increased competitiveness within the dairy industry. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of administrative level on local economic growth from the perspective of officials' rank. We notice an interesting phenomenon that higher‐ranking local officials (HRO) serve in cities with lower administrative level, can effectively improve economic performance similar to the effect of promoting administrative level of local government. Based on prefecture‐level city data from 2001 to 2019, we find that HRO can significantly promote local economic growth, especially in cities with lower levels of development. The mechanism analysis shows that the HRO cities not only could receive upper‐level government's favoritism but also tend to shape good business environment to attract investment. Further, HRO is a softer and shorter way than a formal change of regional administrative level, with less negative impact on the surrounding regions. The results provide new evidence that raising administrative level promotes regional development. [ABSTRACT FROM AUTHOR]

Construction robots are expected to address the challenges of the skilled labor shortage and low productivity in the construction industry. However, the development of construction robots is still in its early stages due to the lack of data on their economic performance, uncertainty in costs, and lack of experience and confidence among companies. This study establishes a cost assessment framework for construction robots. Taking the construction market in China as an example, this study compares and analyzes the construction costs of robotic and traditional construction methods from two groups of cases. Results showed that the current cost of robotic construction does not have an advantage over traditional construction. All construction robots that cost less than traditional methods are more than 2.9 times more efficient than human workers. The use of certain construction robots can reduce the number and skill requirements of construction workers. The construction cost of robot groups under a rational arrangement is lower than that of traditional construction. Further research indicated that the high purchase cost and uncertainty of the life-cycle cost are the main barriers to the application of construction robots. In light of the economic, social, and environmental benefits that construction robots can bring in the future, the current high investment is justified. In conclusion, the cost assessment framework proposed in this study provides construction companies with a systematic approach to selecting construction methods. The cost analysis of the actual construction case serves as a basis for construction companies to utilize construction robots. Practical Applications: The results of this case study suggest that the current cost of robotic construction does not have an advantage over traditional construction. Among the four types of construction robots, those for surveying and marking performed the best in terms of construction cost and those for building decoration performed the worst. All types of construction robots are statistically more efficient than traditional construction methods for the same task, and the high efficiency of construction robots can greatly reduce their construction costs. The use of certain construction robots can reduce the number and skill requirements of construction workers, which in turn reduces labor costs. Rationally arranging robot groups for construction can strengthen the linkage between construction sequences, which in turn increases the utilization of construction robots and accelerates the construction schedule. The results can advise construction contractors on the choice of appropriate construction methods and make recommendations to construction robot manufacturers, construction contractors, and governments for the development and application of construction robots. [ABSTRACT FROM AUTHOR]

Supply chain instabilities and inflated material prices have had a disruptive impact on cost estimating of construction projects. While several research efforts used national macroeconomic indicators to forecast the prices of domestically produced construction materials, none of the existing studies investigated whether the lagged macroeconomic indicators of the main trading partners could enhance the predictability of the prices of cement, steel, and lumber in the US construction sector. This paper fills this knowledge gap. The authors adopted a multi-step methodology that included: (1) collecting data on the target variables and the candidate leading indicators; (2) identifying the structural breaks in the collected data sets; (3) conducting causality tests to identify short-term associations and cointegration tests to examine long-term relationships; (4) developing vector error correction (VEC) models to forecast the prices in the short and long terms; and (5) evaluating the performance of the proposed models against existing forecasting models in the literature. Results of the Granger test and Johansen test indicate that Canada's overall producer price index (PPI) is a consistent leading indicator of the prices of cement, and Mexico's overall PPI is a consistent leading indicator of the prices of steel. Findings indicate no statistical evidence to suggest that neither Canada's PPI nor Mexico's PPI can be leading indicators of lumber prices. Over an 18-month ahead of sample horizon, the presented VEC models of cement and steel prices outperformed existing models, particularly beyond the 1-year-ahead forecasts. Utilization of the proposed forecasting models can significantly enhance the accuracy of cost estimates and feasibility studies of construction projects. This provides proactive financial planning for construction contractors and project owners through improved short- and long-term forecasting of the prices of main construction materials. [ABSTRACT FROM AUTHOR]

This study leverages daily web-scraped data to evaluate the impact of economic sanctions following Russia's invasion of Ukraine on consumer prices and product availability across different goods categories. We find that international sanctions significantly disrupted Russian price dynamics, with the exchange rate serving as the primary transmission mechanism. Utilizing granular online data allows us to circumvent potential misreporting and track real-time economic indicators at high frequency. Our analysis uncovers heterogeneous effects across product groups, with sanctions associated with an average increase of 11.7 percentage points in the Russian CPI. The results highlight how trade policies and geopolitical events can rapidly propagate through retail markets, underscoring the importance of timely price monitoring during periods of economic turbulence. More broadly, we demonstrate the value of online data for evaluating policy shocks, paving the way for similar applications in other contexts. [ABSTRACT FROM AUTHOR]

Shared Autonomous Vehicles (SAV), or robotaxis, are expected to be commercially available within this decade. This new transport mode has the potential to revolutionize travel, offering a level of service comparable to traditional taxis with much lower prices. This may attract travelers currently using other modes, impacting the economic sustainability of public transport as well as car ownership levels. We investigate this potential demand using a scalable SAV simulation framework. We do not establish a future equilibrium considering the interaction between all users on a detailed road network, but establish the potential demand for a large metropolitan area. Travelers can choose between their current mode and the new SAV mode, with fare and waiting times which depend on real-time demand. For our input data we train a statistical model on a large transport survey from Germany for an urban region, allowing us to generate a large number of trips with realistic characteristics. We conduct a sensitivity analysis to study the effect of several key parameters on the modal shift. We find that SAVs can be attractive to many active mode and public transport users unless regulations are put in place. Our results also show that due to SAV fleet constraints, changes in incentives for travelers currently using cars may have significant consequences on the behavior of other travelers. We further calculate key economic indicators for the fleet, which can inform the discussion on the fleet size and fare level that operators are likely to choose when maximizing their own profit. [ABSTRACT FROM AUTHOR]

This study examines the impact of fiscal transparency on governance and corruption within the Middle East and North Africa (MENA) region, with a specific focus on Egypt's standing compared to its regional counterparts. Utilizing data from the Open Budget Survey and the Bayesian Corruption Index, alongside a review of fiscal policies and economic performance metrics, the research highlights the nuanced role of fiscal transparency in combating corruption and fostering effective governance. Findings indicate that higher levels of budgetary transparency correlate with reduced perceptions of corruption, suggesting the critical importance of transparent fiscal practices for governance improvement. The study also explores the relationship between fiscal policies, economic performance, and investment, underlining the significance of transparency in enhancing economic outcomes. This research contributes to the broader scholarly discourse on fiscal governance by providing empirical evidence of the benefits of transparency within the challenging context of the MENA region. [ABSTRACT FROM AUTHOR]

Promoting financial stability is one of the main priorities for the governments of countries seeking to achieve sustainable economic growth. The article aims to assess and model the impact of macroeconomic factors on the financial stability of the Russian Federation in the period 2010-2030 using ADF, OLS, VAR, ARCH, VECM and other techniques. In addition, the linear causal relationship between a group of 6 macroeconomic indicators and the financial stability of the Russian Federation was studied. The results of this research show that Russia's financial stability depends mainly on exports of crude oil and natural gas, price stability, volume of government debt, deficits and surpluses of the state's budget, exchange rate stabilization of national currency, and effectiveness of the banking system. Additionally, events taking place in Eastern Europe, the Middle East and the African continent may negatively affect Russia's financial security if it fails to take the necessary preventive measures. [ABSTRACT FROM AUTHOR]

Copyright of Perspektiven der Wirtschaftspolitik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The tourism and hospitality (T&H) industry can significantly impact the environment by using natural resources and waste generation. Measuring and improving firms' emission performance in this industry is becoming increasingly important. However, the prediction of financial indicators and governance scores on firms' emission performance is still poorly established. Drawing on the agency and stakeholders' theory, our research explores whether financial indicators and governance scores predict firms' emission performance in the T&H industry. Data on all companies that are publicly traded was acquired from Thomson Reuters Eikon using 485 publicly traded tourism firms as of the end of the fiscal year 2021. Our findings suggest that governance pillar score (GP), management score (MS), board size (BS), and market capitalization (MC) are the best four predictors of carbon emission reduction scores in T&H companies. Theoretical and practical implications are presented, and directions for further research are provided. [ABSTRACT FROM AUTHOR]

In order to reduce overtourism and traffic congestion, local authorities may have to divert recreational vehicle traffic to off-site service areas. The problem that will arise is how best to accommodate different types of users with opposing preferences in the same area, some wanting to be as close as possible to the major site to be visited, others seeking peace and quiet. We have represented their specific attitudes using a two-stage decision-making process via a conjunctive model followed by a compensatory model. We then propose to model three strategies, seeking either to optimise customer attractiveness, or profit, or space occupation, in order to define a location, capacity and price for this shared area. Using a realistic data set, the results show that economic performance follows a concave curve as a function of the population mix. Moreover, only the strategy of maximising attractiveness suggests always mixing users in the same area. [ABSTRACT FROM AUTHOR]

In this study, we investigate the impact of introducing enterprise relationship to train machine learning (ML) algorithms on improving its ability of forecasting small and medium-sized enterprises' (SMEs) credit risk in supply chain finance (SCF). First, we incorporate attributes of SME node in the CEs-SMEs network, a bipartite network constructed with transaction data in the Chinese automobile industry, into financial indicators to train ML algorithms for improving forecasting performance. Second, we employ the weighted one-mode projection approach to extract a projected-SMEs network that reveals the relationship among SMEs from the CEs-SMEs network and examine whether the ML algorithms' forecasting performances is improved when considering attributes in the projected-SMEs network. Third, we analyze the correlation between SME's credit risk and individual network attribute to specifically understand the impact of enterprise relationship on credit risk. Overall, the empirical results indicate that the forecasting performance is obviously better when enterprise transaction relationship is considered, and it is further enhanced after we apply weighted one-mode projection approach. Meanwhile, the variable importance analysis and the binomial logistic regression demonstrate how significantly each network attribute is correlated with credit risk, and the discussion on partial dependence plot shows that SMEs with large degree are non-risky generally. [ABSTRACT FROM AUTHOR]

Purpose: This study attempts to explore the association between circular economy implementation and sustainability performance for the textile industry in Vietnam. This study also investigated the influences of corporate green commitment on implementing circular economy practices as well as the moderating effect of corporate green efficacy on the relationship between corporate green commitment and circular economy implementation. Design/methodology/approach: The circular economy practices in the study were divided into three categories: internal environmental management, eco-design, and investment recovery. Sustainability performance consists of economic performance, environmental performance, and social performance. A questionnaire survey on Vietnam's textile companies was conducted in the study to test the proposed research framework. Findings: Research findings reveal that corporate green commitment significantly influences the implementation of circular economy practices, and circular economy practices have significantly positive influences on sustainability performance. Corporate green efficacy significantly moderates the association between corporate green commitment and circular economy implementation. Originality/value: As one of the first empirical studies focusing on the circular economy issues in Vietnam's textile industry, this study extended the understanding of the antecedents and consequences of implementing circular economy practices. The research results are theoretically and practically meaningful. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: Digital transformation (DT) has great potential to improve operations and stimulate business growth. It has attracted widespread attention from businesses around the world. However, firm performance in DT implementation (DTI) cannot be neglected. This study examines the benefits and costs of DT and its effects on operations and financial performance of DT. Design/methodology/approach: Based on the financial report data from S&P 500 firms, we measure the financial performance, productivity and market value of DTI with a baseline in 2020. In addition, we collect secondary data to construct linear regression models for three dummy variables, including DT adopters, first movers and better performers. Findings: Empirical results demonstrate that digitally transformed firms are positively correlated with business performance, while the performance among first movers has a partially positive impact. DT has also had a significant impact on long-term performance as shown by our long-term observations. Empirical findings indicate that DT has varying impacts on organizational performance in different dimensions and provides DT target-setting guidance to enterprises. Originality/value: There are no studies so far that could proffer certain evidence to reveal the influence of DTI on firm performance. Such evidence is necessary for managers to convince their investors and stakeholders to believe that DTI can create new revenue for enterprises. [ABSTRACT FROM AUTHOR]

In the framework of law and finance literature, this study investigates the link between judicial efficiency and financial debt cost in 1487 non‐financial firms across the euro zone from 2010 to 2021. Utilizing qualitative (rule of law) and quantitative (judicial proceeding length) indicators from the Worldwide Governance Indicators and Doing Business databases, our findings support the hypothesis that efficient justice lowers financial debt cost. Additionally, we observe a moderating effect of debt structure, specifically that private non‐bank debt enhances the reduction impact of judicial efficiency on debt cost, unlike bank and corporate bonds debt. [ABSTRACT FROM AUTHOR]

China and India are both late comers to industrialization. Both adopted similar economic development strategies after World War II, but the per capita GDP diverged significantly in the last 40 years. While economic growth and development have many components, we explain the difference in economic performance by emphasizing the difference in state capacity in the two economies. A country's state capacity is affected by culture and history. China established a unified language and culture two thousand years ago that enabled it to develop strong state capacity. With a strong state capacity, China made crucial investments in infrastructure and in key heavy industries and developed technological capabilities to help start and sustain growth. India, on the other hand, is a country segmented by religion, caste, and language which has hindered the development of effective state capacity, and thus complementary state investments to spur economic growth. Moreover, India has up to now relied more heavily on expansion of its service sector compared to China, which has hindered its exports, a crucial element that helped China's economy. India's future industrialization crucially depends on national integration and concomitant strengthening of state capacity. [ABSTRACT FROM AUTHOR]

This paper aims to empirically explore the linkage between the cooperative sector and economic growth in Italian provinces (NUTS‐3), through a panel analysis covering the period between 2013 and 2019. We hypothesize that the cooperative sector can contribute to economic growth through its ability to generate social capital, thereby helping to mitigate market failures. The empirical analysis does not provide us with robust evidence of the positive connection between the cooperative sector as a whole and local economic growth. However, when we disaggregate the cooperative sector into ordinary and social cooperatives, we find intriguing results. Indeed, there is robust evidence of a negative relationship between the territorial distribution of ordinary cooperatives and economic growth, while for social cooperatives we find a positive relationship. We interpret these results as showing that the pursuit of general interest objectives, which allows the principle of mutuality to be extended to indirect forms of reciprocity, could make cooperative firms more effective in promoting the economic performance of territories. This is because they are more likely to generate bridging social capital that encourages the spread of generalized trust, a key factor in the advancement of modern market economies. [ABSTRACT FROM AUTHOR]

Rural collective‐owned cooperatives (RCOCs) have received significant attention for rural development, alleviating rural poverty, and achieving common welfare in China. Using the Social‐ecological system (SES) framework as a conceptual and analytical lens, this paper clarifies the determinants and the influence mechanism for the economic performance of RCOCs. A mixed method, PLS‐SEM combined NCA approach is applied to 420 smallholder farmers in rural China. The results reveal that rural collective property‐rights system reform (RCPRSR), leadership, social capital, and collective action positively affect the economic performance of RCOCs. Moreover, leadership and social capital also operate through their mediating effect on collective action amongst collective members. Our results further show that RCPRSR, leadership, and social capital are necessary conditions for the high economic performance of RCOCs. [ABSTRACT FROM AUTHOR]

The food-manufacturing industry is one of the major energy- and resource-consuming sectors, and this increase in energy consumption contributes significantly to greenhouse gas emissions. Life cycle assessment is a comprehensive method that is widely used to quantify the energy requirements and environmental impacts of a product. Poultry industry in Iran is one of the largest and most developed industries in the country. With the increasing population and increasing demand for healthy meat, the expansion and development of the poultry industry to meet protein needs seems essential. Therefore, the purpose of this study was to determine the energy, environmental and economic indicators of turkey meat production in West Azarbaijan and Zanjan province, Iran, which can help to improve energy consumption and reduce bio-pollutants entering the environment and increase economic productivity. According to the results, the average amount of energy consumed in turkeys and slaughterhouses was calculated to be 1185.55 and 225.90 GJ per 1000 pieces, respectively. Three environmental indices including the global warming potential, acidification index and eutrophication index in turkey breeding units were calculated as 1841.64 kg of equivalent carbon dioxide, 37.68 kg equivalent sulfur dioxide and 14.05 kg of equivalent phosphate for one ton live turkey. According to this study, it seems that LCA can be a good approach for different managements on environment. [ABSTRACT FROM AUTHOR]

Previous studies examined the impact of finance on CO2 emissions; however, relatively few studies have explored the role of green investment in carbon reduction. In this study, we assessed the relationship between green investment, high-quality economic development, and carbon emissions in China. Based on the evaluation of three first-level indicators of economic development, social life, and ecological environment from 2001 to 2020, the index of China's high-quality economic development level was calculated and performed analysis using an autoregression-distributed lag error-correction model. The results confirmed a long-term cointegration relationship between the three variables in China. In the long term, green investment is conducive to high-quality economic development and carbon-emissions reduction. During the process of high-quality economic development, carbon emissions will be reduced, and high-quality economic development and carbon emission reduction are also conducive to the increase of green investment. Second, in the short term, high-quality economic development and green investment demonstrate different mechanisms of influence on carbon emissions during the lag period. These findings suggest that investment in green energy should be strengthened, and the concept of green development should be adhered to in order to promote green economy to help improve China's environmental quality. [ABSTRACT FROM AUTHOR]

Abstact: In this paper we develop nowcasting models for the Pays‐de‐la‐Loire's jobseekers, a dynamic French regional economy. We ask whether these regional nowcasts are more accurate by only using the regional data or by combining the national and regional data. For this purpose, we use penalized regressions, random forest, and dynamic factor models as well as dimension reduction approaches. The best nowcasting performance is provided by the DFM estimated on the regional and regional‐national databases as well as the Elastic‐Net model with a prior screening step for which the national data are the most frequently selected data. For the latter, it appears that the Change in foreign orders in the industry sector, the OECD Composite leading indicator, and the BdF Business sentiment indicator are among the major predictors. [ABSTRACT FROM AUTHOR]

The impact of the Russian-Ukrainian war on energy prices contributed significantly to European price increases in 2022. The study aims to find a linkage between the performance of 24 EU countries during the energy inflation crisis and their preparedness, vulnerability or exposure. The verified hypotheses reflect on the role of initial conditions of countries and the one-year impact of energy inflation on their economic performance. The two-step analysis first creates six clusters of countries based on their energy, trade, financial and political vulnerability, and preparedness indicators. The second step is to explore the shifts of clusters in expectations on macroeconomic indicators. Specific patterns of country groups are explored in the value and evolution of wartime indicators of inflation, GDP growth, consumer and business confidence, as well as FX volatility. The exploration concludes that the entry variables of clustering are relevant, and the EU countries can be segmented by dependency, energy, financial, and political aspects. Thus, it is possible to verify the distance in risk and exposure among EU economies. The impact variables demonstrated that the extent of the inflationary effect depended on the initial conditions. In addition, the research identified protective short-term factors against energy inflation originating in a trade and war context. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the paper is to examine the status and determinants of financial inclusion in India by using the recent micro-level survey data. Design/methodology/approach: The authors construct a multidimensional financial inclusion index to measure the status of financial inclusion in the selected 17 states of India. Subsequently, the authors use the probit model estimation to examine the determinants of all financial inclusion indicators. Findings: The authors find that southern and north-eastern states perform better in the overall financial inclusion index. In contrast, states like Odisha, Madhya Pradesh, Chhattisgarh, Rajasthan and Uttar Pradesh lag behind. The estimated result shows that the probability of being financially included is higher among urban, richer, educated and salaried individuals. Further, the findings indicate the lower penetration of bank branches and ATMs in the rural parts of the country. Originality/value: While numerous studies have explored financial inclusion from a macro-level perspective, there exists a notable gap in the literature at the micro-level. This paper aims to address this gap and contributes to the existing literature in two ways. Firstly, it uses the recent micro-level survey data to construct a multidimensional financial inclusion index for the selected Indian states. Secondly, it examines individual-level attributes as the determining factors of financial inclusion, which has been overlooked in India. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0162 [ABSTRACT FROM AUTHOR]

This is a sustainable study of China amidst high carbon emissions. China has experienced tremendous increase in its economic operations and development which involve the excessive utilization of fossil fuel energy sources. This has put China in the list of nations that contribute towards global warming via carbon emission. On this note, data from China over the period 2002Q1–2019Q4 is analyzed, using multiple techniques (nonlinear autoregressive distributed lag-NARDL, fully modified ordinary least square-FMOLS, and bootstrap approach of Granger causality) for clear insight into China's sustainable development. Relevant instruments (technological innovation, entrepreneurial activities, economic growth proxied by GDP, fossil fuel energy consumption, and FDI) are used to measure China's economic and environmental performance to determine the level of sustainable development of the country. NARDL and FMOLS results reveal that technological innovation and entrepreneurial activity mitigate carbon emissions, while fossil fuels and economic growth induce China's carbon emissions. Also, findings from the bootstrap approach affirm the NARDL and FMOLS outcomes, with both two-way and one-way nexus established among the selected variables. Policies targeting the reduction of fossil fuel consumption in China despite the technological innovations and entrepreneurial activities are thus recommended. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic has raised concerns about the global capacity for timely outbreak reporting. However, gaps remain in our understanding of barriers and enablers to outbreak reporting, particularly at the local level. Field epidemiology training program (FETP) fellows often participate in the outbreak reporting process as part of both their training and the public health roles they assume after graduating; they therefore represent a potentially valuable source of information for better understanding these barriers and enablers. This study will investigate the barriers and enablers to outbreak reporting through a mixed methods approach that will encompass a review of the existing literature as well as surveying and interviewing FETP trainees and graduates from the Asia-Pacific region. Methods: This study will begin with a scoping review of the literature to identify existing evidence of barriers and enablers to outbreak reporting. Based on our findings from the scoping review, we will administer a survey to FETP trainees and graduates from the World Health Organization Western Pacific and South-East Asian Regions and conduct interviews with a subset of survey respondents to investigate the survey findings in more detail. We will summarise and compare the survey results according to various country-level economic and political indicators, and we will employ thematic analysis to evaluate the interview responses. Based on the findings from the scoping review, survey, and interviews, we will construct a model to comprehensively describe the various barriers and enablers to outbreak reporting. Conclusion: This study will contribute to our understanding of the determinants of outbreak reporting across several geographic, political, and economic contexts by eliciting the viewpoints and experiences of persons involved with outbreak reporting, particularly at the local level. This information will help improve the outbreak reporting process, allowing for more timely reporting and helping prevent future outbreaks from becoming pandemics. [ABSTRACT FROM AUTHOR]

Economic growth is essential for regional economic performance, with gross regional domestic product (GRDP) being a key indicator of economic development over time. In this research case, the GRDP data of various provinces on Java Island from 2010 to 2023 will be used as the variable being studied. The data obtained from the GRDP variable contain spatial and temporal information, requiring an appropriate model to forecast spatiotemporal data, namely, the Generalized Space-Time Autoregressive (GSTAR) model. However, in estimating the parameters, the GSTAR model is unable to detect correlated residuals between equations, resulting in inefficient estimators. Therefore, an appropriate estimation method is needed to address correlated residuals within the seemingly unrelated regression (SUR) framework, namely, the Generalized Least Square (GLS) estimation method. The GSTAR-SUR method is applied to forecast the economic growth rate of Java Island. The optimal model, GSTAR-SUR (1[sub 1])-I(1) with inverse distance location weights, demonstrates high accuracy with a mean absolute percentage error (MAPE) of 8.451%. Forecasts for Banten, DKI Jakarta, West Java, Central Java, East Java, and DI Yogyakarta predict consistent monthly GRDP increases through December 2024. [ABSTRACT FROM AUTHOR]

In order to study the relationship between China's safety production indicators and economic and social indicators, the development trend of indicator data in the past 20 years was statistically analyzed, and qualitative and quantitative research was conducted using grey relational analysis and multiple linear regression analysis methods. In the past two decades, there has been a significant improvement in the number of deaths, work-related injuries, and occupational patients in China's safety production, and the country's three categories of 14 economic and social indicators have achieved rapid development. Using the grey relation analysis method, the grey correlation degree between the number of deaths, work-related injuries, and occupational patients in China over the past twenty years and 14 economic and social indicators was obtained. The ranking of economic and social indicators that affect the number of deaths, work-related injuries, and occupational patients varies greatly. A multiple linear regression model was established for the number of deaths, work-related injuries, occupational diseases, and 14 economic and social indicators. The rationality of the model was verified from four aspects: R2, F-value, P-value, and deviation between actual and fitted values. Provide guidance for the development of safety production indicators and economic and social indicators in China through research. [ABSTRACT FROM AUTHOR]

Variation in political preferences is increasingly understood to stem from both environmental influences and genetics. Research in political psychology has argued that a pathway for genetic effects on ideology is via cognitive performance, showing a sizable genetic overlap between the traits. Yet the link between actual trait cognitive performance and economic conservatism is itself highly ambiguous, with both positive, null, and negative estimates prevalent in the literature. In this study, I argue that this puzzle can only be understood from a gene–environment interaction (GxE) perspective. Drawing on traditional theories of political preference formation, I argue that genetics associated with cognitive performance should cause more left‐wing economic preferences if you grow up in relative poverty, but more right‐wing economic preferences if you grow up affluent. Utilizing variation in a polygenic index (PGI) of cognitive performance within dizygotic twin pairs, coupled with unique register data on economic conditions for the twins, their parents, and their childhood neighborhood, I show that the causal effect of the PGI on economic conservatism is zero on average, but indeed sizable and sign‐discordant by class background. The GxE perspective thus has wide‐ranging implications for future research attempting to integrate genetic methods into political psychology. [ABSTRACT FROM AUTHOR]

Background: Management of sedation, analgesia, and delirium influences morbidity, mortality, and quality of life in patients treated in intensive care. Assessing quality indicators as part of a quality management and assurance program is an established method to ensure process quality. Currently, there is limited research on the effect of evaluating quality indicators on economic outcomes. The aim of the study was to investigate the adherence to an indicator on management of sedation, analgesia and delirium, and explore potential effects on hospital economics and clinical outcomes. Methods: In this retrospective cohort study, we analyzed routine data from 20,220 patient records from the hospital information system of a tertiary university hospital, collected from January 2012 to December 2019. We compared two predefined subgroups with either high indicator adherence or low indicator adherence regarding factors like disease severity scores, comorbidities, and outcome measures. We used logistic regression models to examine the influence of quality indicator adherence on economic measures such as Diagnosis-related group (DRG) incomes, revenue margins, and costs, and clinical outcomes. Additionally, we used propensity score matching to probe our findings. Results: Overall revenue margins in this cohort were negative (-320€). High adherence to the quality indicator was associated with a positive revenue margin (+197€) compared to low adherence (-482€). Higher adherence was also associated with lower costs. Additionally, high adherence was associated with reduced mortality (OR 0.84, 95% CI 0.75–0.95) and reduced duration of mechanical ventilation and hospital stay (17 hours and 1 day respectively). Conclusion: Higher adherence to a quality indicator for sedation, analgesia, and delirium management was associated with economic returns and costs. We also found an association with lower mortality and reduced length of stay. Further research on these associations may help identify opportunities for quality improvement without increased resource use. [ABSTRACT FROM AUTHOR]

This study uses fuzzy–rough analysis to investigate the influence of Environmental, Social, and Governance (ESG) ratings, along with critical financial and growth ratios, on the stock returns of blue-chip companies in Taiwan. The growing importance of ESG factors in investment decisions underscores the need to understand their impact on stock performance. By integrating the fuzzy–rough set theory, which accommodates uncertainty and imprecision in data, we analyze the complex relationships between ESG ratings, traditional financial metrics (such as ROE, return on equity), and stock returns. Our findings provide insights into how ESG considerations, alongside financial indicators, drive the returns of Taiwan's blue-chip stocks. Three public-listed companies were evaluated using this approach, and the results are consistent with the actual stock performance. This research contributes to the field by offering a robust methodological approach to assess the nuanced effects of ESG factors on financial performance, thus aiding investors and management teams in making informed decisions. [ABSTRACT FROM AUTHOR]

Physical capital stock is the basic indicator of macroeconomic empirical studies and also an important manifestation of macroeconomic activity capacity. When measuring China's provincial physical capital stock by the perpetual inventory method, the treatment for the depreciation rate and the initial physical capital stock in the existing literature has some defects in reflecting the economic performance. The purpose of this paper is to build the most reliable and reasonable statistical series of China's provincial physical capital stock. We establish a measurement formula in form of the variable depreciation rate and improve the treatment for the depreciation rate and the initial physical capital stock. For the depreciation rate, we measure the benchmark depreciation rate of physical capital and determine the variable depreciation rate according to the economic growth rate, which makes the variable depreciation rate reflect the economic growth. For the initial physical capital stock, we measure the initial physical capital stock according to the capital output ratio, which makes the initial physical capital stock reflect the economic aggregate. Based on these improvements, we measure China's provincial physical capital stock from 1952 to 2022 by the perpetual inventory method. Through analyzing the measurement results, we have a more distinct insight into the temporal and spatial characteristics of China's provincial physical capital stock: The growth rate of physical capital stock at the provincial level has slowed in spite of remaining high; The eastern region is much higher than the central and western regions. According to these two characteristics, it is suggested that policymakers need to improve market mechanisms and adjust investment policies to promote a benign profile of economic growth and a regional optimization of capital formation. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Indicators of surgical outcomes are designed to objectively evaluate surgical performance, enabling comparisons among surgeons and institutions. In recent years, there has been a surge in complex indicators of perioperative short‐term and long‐term outcomes. The aim of this narrative review is to provide an overview and a critical analysis of surgical outcomes indicators, with a special emphasis on hepato‐pancreato‐biliary (HPB) surgery.A narrative review of outcome measures was conducted using a combined text and MeSH search strategy to identify relevant articles focused on perioperative outcomes, specifically within HPB surgery.The literature search yielded 624 records, and 94 studies were included in the analysis. Included papers were classified depending on whether they assessed intraoperative or postoperative specific or composite outcomes, and whether they assessed purely clinical or combined clinical and socio‐economic indicators. Specific indicators included in composite outcomes were categorized into three main domains: intraoperative metrics, postoperative outcomes, and oncological outcomes. While postoperative mortality, complications, hospital stay and readmission were the indicators most frequently included in composite outcomes, oncological outcomes were rarely considered.The evolution of surgical outcomes has shifted from the simplistic assessment of crude mortality rates to complex composite outcomes. Whether the recent explosion of publications on these topics has a clinical impact in real life is questionable. Outcomes from the patient perspective, integrating social and financial indicators, are not yet integrated into most of these composite analytical tools but should not be underestimated. [ABSTRACT FROM AUTHOR]

A hydrogen fueling station is a facility designed to support the widespread use of hydrogen energy for the automotive industry. Substituting the use of fossil fuel in the road transport sector with hydrogen fuel generated from renewable sources can lead to environmental advantages. This study investigated a comprehensive analysis of three hybrid energy systems e.g., Photovoltaic -wind turbine-fuel cell-battery, Photovoltaic-wind turbine-battery, and Wind turbine-fuel cell-battery for hydrogen production and refueling station in the city of Muscat using Hybrid Optimization Model for Electrical Renewable (HOMER) software that assesses the viability of energy system to meet the required energy needs. The evaluation of the hybrid energy arrangements relies on the assessment of the renewable energy capacity at the location, the expenses, and optimization associated with different components, and the hydrogen demand. The results of the techno-economic analysis showed that the photovoltaic-wind turbine-battery energy system has the lowest net present cost (NPC), levelized cost of energy, and levelized cost of hydrogen of $ 529,361, $/kWh 0.0158, and $/kg 0.401 respectively, adjudging the system to be the most viable system for the refueling station in Muscat. The optimized result showed that the P-WT-B energy system comprised of 1000 kW of PV panel, 660 kW wind turbine, a 500 kW electrolyzer, a 100 1 kW h battery, a 1000 kg/L hydrogen tank, and a 492-kW converter that offered the lowest cost of energy, hydrogen, and net present cost compared to other energy systems. Furthermore, the return on investment and annual worth analysis positioned the Photovoltaic-wind turbine-battery energy system to be the best bet and optimal choice for the Muscat refueling station project. • Photovoltaic/Battery energy system was proposed for the city of Muscat, Oman. Renewable energy usage contributes to Oman's Vision 2040. • The integration of renewable energy systems has significantly increased energy production. • Economic indicators might be utilized to ascertain the optimal choice for energy utilization. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Within urban and tourism studies, although much research has focused on uneven city prosperity and competitiveness, little is known about factors influencing seaside town economic performance. We adopt a place-based approach to understand its determinants amongst 58 of England's largest seaside towns drawing on a bespoke database. Through Partial Least Square analysis, Spearman's Rank Correlation and general linear regression modelling for panel data with random effects, we identify the 'leaders' and 'laggers' along with a set of associated socio-economic characteristics. These insights enhance understanding of how and why economic performance differs amongst these towns. We highlight implications for addressing such socio-economic disparities across seaside towns, which has wider destination relevance, and use these findings to inform policy which seeks to raise the productivity potential of 'lagging' towns. [ABSTRACT FROM AUTHOR]