The main objectives of this research are to improve our understanding of energy-climate-manufacturing nexus within the context of regional and global manufacturing supply chains as well as show the significance of full coverage of entire supply chain tiers in order to prevent significant underestimations, which might lead to invalid policy conclusions. With this motivation, a multi-region input-output (MRIO) sustainability assessment model is developed by using the World Input-Output Database, which is a dynamic MRIO framework on the world's 40 largest economies covering 1440 economic sectors. The method presented in this study is the first environmentally-extended MRIO model that harmonizes energy and carbon footprint accounts for Turkish manufacturing sectors and a global tradelinked carbon and energy footprint analysis of Turkish manufacturing sectors is performed as a case study. The results were presented by distinguishing the contributions of five common supply chain phases such as upstream suppliers, onsite manufacturing, transportation, wholesale, and retail trade. The findings showed that onsite and upstream supply chains are found to have over 90% of total energy use and carbon footprint for all industrial sectors. Electricity, Gas and Water Supply sector was usually found to be as the main contributor to global climate change, and Coke, Refined Petroleum, and Nuclear Fuel sector is the main driver of energy use in upstream supply chains. Overall, the largest portion of total carbon emissions of Turkish manufacturing industries was found in Turkey's regional boundary that ranged between 40 to 60% of total carbon emissions. In 2009, China, United States, and Rest-of-the-World's contribution is found to be more than 50% of total energy use of Turkish manufacturing. The authors envision that a global MRIO framework can provide a vital guidance for policy makers to analyze the role of global manufacturing supply chains and prevent significant underestimations due to inclusion of limited number of tiers for sustainable supply chain management research.

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Abstract:The metropolitan city of Istanbul is becoming overcrowded and the demand for clean water is steeply rising in the city. The use of analytical approaches has become more and more critical for forecasting the water supply and demand balance in the long run. In this research, Istanbul's water supply and demand data is collected for the period during 2006 and 2014. Then, using an autoregressive integrated moving average (ARIMA) model, the time series water supply and demand forecasting model is constructed for the period between 2015 and 2018. Three important sustainability metrics such as water loss to supply ratio, water loss to demand ratio, and water loss to residential demand ratio are also presented. The findings show that residential water demand is responsible for nearly 80% of total water use and the consumption categories including commercial, industrial, agriculture, outdoor, and others have a lower share in total water demand. The results also show that there is a considerable water loss in the water distribution system which requires significant investments on the water supply networks. Furthermore, the forecasting results indicated that pipeline projects will be critical in the near future due to expected increases in the total water demand of Istanbul. The authors suggest that sustainable management of water can be achieved by reducing the residential water use through the use of water efficient OPEN ACCESSSustainability 2015, 7 11051 technologies in households and reduction in water supply loss through investments on distribution infrastructure.

Introduction: Work-related fatigue is a source of concern, even in most industrialized countries. One of the most important factors influencing an employee's physical and mental condition is the degree to which employees are able to recover from fatigue and stress after work. Factors such as workload can cause fatigue in workers. The aim of this study was using the need for recovery scale to assess workload in mine workers and its relationship with demographics. Methods: In this cross-sectional study, 80 workers of a mine were surveyed. The data gathering tools used in this study were demographic characteristics questionnaire and the need for recovery scale. The scale assesses the workers workload with 11 two-optioned phrases. Mean, standard deviation, and Pearson's correlation coefficient and ANOVA tests were used in order for data analysis. Results: Mean (SD) score of the need for recovery scale was 55.22 (23.93), indicating moderately high workload in the workers. A total of 58.7% of workers experienced high levels of workload. Among the demographics, only body weight had a significant relationship with the need for recovery score (P value = 0.043). Conclusions: Due to the relatively high need for recovery in the study population, solutions should be employed, such as reducing the workload, use of work-rest schedules, performing heavy tasks within teams, and providing conditions for proper and enough recovery after work, which can increase health conditions of workers.