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Material extrusion (MEX) is a class of additive manufacturing (AM) process based on MEX principle. In the viewpoint of Industry 4.0 and sustainable manufacturing, AM technologies are gaining importance than conventional manufacturing route (subtractive manufacturing). Because of the ease of use and lesser operation skills, MEX had wide popularity in industry for product and prototype development. This study aims to analyze energy consumption of MEX-based AM process and its influencing factors.

A group of factors were identified pertaining to MEX-based AM process. In this viewpoint, this study presents the configuration of a structural model using interpretive structural modeling (ISM) to depict dominant factors in MEX-based AM process. A total of 18 influencing factors are identified and ranked using ISM methodology for MEX process. The Impact Matrix Cross-reference Multiplication Applied to a Classification analysis was done to categorize influencing factors into four groups for MEX-based AM process.

The derivation of structural model would enable AM practitioners to systematically analyze the factors and to derive key factors which enable comprehensive energy modeling and energy assessment studies. Also, it facilitates the development of energy efficient AM system.

The development of structural model for analysis of factors influencing energy consumption of MEX-based AM is the original contribution of the authors.

The calculation of buildings’ carbon footprint (CFP) is an important basis for formulating energy-saving and emission-reduction plans for building. As an important building type, there is currently no model that considers the time factor to accurately calculate the CFP of hospital building throughout their life cycle. This paper aims to establish a CFP calculation model that covers the life cycle of hospital building and considers time factor.

On the basis of field and literature research, the basic framework is built using dynamic life cycle assessment (DLCA), and the gray prediction model is used to predict the future value. Finally, a CFP model covering the whole life cycle has been constructed and applied to a hospital building in China.

The results applied to the case show that the CO₂ emission in the operation stage of the hospital building is much higher than that in other stages, and the total CO₂ emission in the dynamic and static analysis operation stage accounts for 83.66% and 79.03%, respectively; the difference of annual average emission of CO₂ reached 28.33%. The research results show that DLCA is more accurate than traditional static life cycle assessment (LCA) when measuring long-term objects such as carbon emissions in the whole life cycle of hospital building.

This research established a carbon emission calculation model that covers the life cycle of hospital building and considered time factor, which enriches the research on carbon emission of hospital building, a special and extensive public building, and dynamically quantifies the resource consumption of hospital building in the life cycle. This paper provided a certain reference for the green design, energy saving, emission reduction and efficient use of hospital building, obviously, the limitation is that this model is only applicable to hospital building.

In working towards a sustainable campus of public universities, energy consumption behaviour assessment is one of the several issues that requires attention by the facilities manager. Information on energy consumption behaviour is needed to determine potential energy savings. The purpose of this study is to assess energy consumption behaviour for student accommodations in Malaysian public universities.

This study focuses on developing energy consumption behaviour models (ECBMs) and assesses the potential energy savings. The “energy culture” framework consolidated with multiple regression analysis is used to strengthen the development of ECBMs. A self-administrated survey involving 1,009 respondents in selected public universities was carried out.

The result shows that five factors from the energy culture framework contribute to energy consumption behaviour, namely, building regulation, environmental concern, education, social marketing and direct factors (device and activities). These factors are included in the model for predicting energy consumption levels. The results show that there is a 78 per cent difference in energy consumption between the observed and predicted data.

This study indicates a high potential energy saving among students of Malaysian public universities.

The model was tested against the overall students among Malaysian public universities. In future, the model can be tested within hostel accommodations. The present assessment revealed the potential energy saving among the hostel buildings and sets the target regarding which building has a potential to reduce energy. It also helps the facilities managers to come up with strategies for programmes and energy policy in public universities.

With the function of reconstructing and promoting traditional industries in China, logistic service industry (LSI) still consumes a great deal of energy. The purpose of this paper is to empirically analyse the relationship between energy consumption of logistics and its influential factors in China, and through this, identify the most important factor and give a significant research of this factor afterwards.

Using a quantitative analysis of the industry composition of energy consumption and the structure of energy consumption in LSI, the serious condition of China's energy conservation of LSI was found. Correlated analysis and co‐integration analysis were adopted to identify how these factors affected the energy conservation of LSI and how to construct environmental logistics in China.

The paper found mileage traveled by train is the most important factor of energy consumption in LSI, while traveling miles of highway is the second. The former was negative correlation with the total of energy consumption while the latter was positive correlation.

Because of the deficiency of data in China, this research is based on the data of 30 years, which is not testified as long enough for this research. This would indicate that the conclusions of this paper are approximate values. A closer look should be taken at the effectiveness of some of the other factors suggested in the previous research on this topic.

The paper outlines ways to effectively construct environmental logistics in China which would promote the energy conservation and sustainable development of this country. Since the total consumption of energy in China accounts for a certain proportion of the world, this will benefit other countries as well.

This paper is one of the first of its kind to develop and empirically analyze the relationship between energy consumption of LSI and its influential factors in China. It uniquely contributes towards helping us find approaches to constructing China's environmental logistics and will have far‐reaching implications for other developing countries.

This research has addressed a quantitative approach for improving energy management through applying statistical techniques aimed at identifying and controlling factors linked to energy consumption rates at manufacturing plants. The paper presents analysis and results of multiple linear regression models used to establish the significance of a number of energy related management factors in controlling energy usage. Regression models constructed for this purpose proved the existence of statistically valid relationships between electrical energy consumption and maintenance and production management factors, namely, failure rate and production rate, where R² values of the magnitude of 65 per cent were obtained. Furthermore, an economical treatment based on the derived regression models was formulated and demonstrated that effective management practices associated with proper maintenance, cost accounting and reporting systems can result in highly significant savings in energy usage.

The rail transport sector in China represents one of the largest consumers of energy today, and the primary purpose of this paper is to examine the causes of changes in energy consumption of Chinese national rail transport (ECCNRT).

For this study, reasonable indicators as factors that affected conversion volume (CV) and unit energy consumption (UEC) based on statistical data from 1990 to 2010 were selected. CV and UEC models were established by regression analysis and tested using real data of 2011-2014. The CV model indicates it has an exponential relationship with GDP. Besides, there is a quantitative relationship between UEC and the quantity of locomotives. The ECCNRT calculation model was proposed and ECCNRT model data were compared with the real data. Impacts of different factors on ECCNRT were analyzed with economic principles.

The analysis conducted shows that the calculation model can reflect variation of ECCNRT precisely, and ECCNRT has a quantitative relation with GDP and quantities of locomotives. GDP accounts for changes of ECCNRT 20.02 per cent, while those for quantity of diesel locomotives and electric locomotives are 26.87 and 53.11 per cent, respectively. The number of electric locomotives is the main factor that influences variation of ECCNRT.

Through regression analysis, this study discovered the inner quantitative relationship between the conversion volume (important index of Chinese national rail transport production) and GDP. In addition, this study establishes the ECCNRT model according to the Chinese national rail transport data, which can be used to calculate the amount of ECCNRT and conduct quantitative analysis for different impacts of various factors on ECCNRT’s changes.

The energy efficiency policy is a priority component of the overall economic policy of different countries striving to ensure the competitiveness and sustainability of national economic development. The improvement of energy efficiency represents an important economic task for the post-Soviet countries, characterized by excessive energy intensity of the economy, and the solution of this task requires proper information and analytical support: a system for accounting and analyzing energy consumption indicators. The present research is aimed at developing the tools to support decision-making in the sphere of evaluation and estimation of performance of the State energy efficiency policy of territories and testing these tools on the example of Russian regions. The study has been carried out using the methods of statistics, economic, mathematical and econometric modeling, structural, dynamic and comparative analyses. The following tools have been proposed: the method for differentiated accounting of various factors’ influence on the dynamics of energy consumption in the regions and for estimating the index of technological efficiency of electricity consumption; the method for the empirical classification of territories by types of their energy and economic development. We’ve revealed the general trend and typological features in the dynamics of electricity consumption efficiency indicators in the constituent entities of the Russian Federation and carried out the decomposition factor and comparative analysis of energy consumption patterns of the Volgograd region over 2005–2014 on the basis of the proposed tools.

The purpose of this study was to explore the relationship between environmental factors and building energy consumption of three Leadership in Energy and Environmental Design (LEED)-certified buildings at the Arizona State University, by establishing the relationships of the outside atmospheric temperature and the energy consumed in the building using real-time data generated from different sources.

K-means clustering analysis is used to calibrate and eliminate unwanted influences or factors from a set of building consumption real-time data. For further statistical analysis, the chi-square is used to verify if the results are ample to prove the findings.

Few studies have addressed building energy consumption real-time data versus LEED Energy and Atmosphere (EA) credits with the data mining technique (k-means clustering) on most of building performance analyses. This study highlighted that the calibrating energy data are a better approach to analyze energy use in buildings and that there is a relationship between LEED credits’ (EA) Optimize Energy Performance scores and building energy efficiency. However, the energy consumption data alone do not yield useful results to establish the cause and effect relationships.

Although there are several previous research studies regarding LEED building energy performance, this research study focused on the LEED building energy performance versus LEED EA credits versus environmental factors using real-time building energy data and various statistical methods (e.g. K-means clustering and chi-square). The findings provide researchers, engineers and architects with valuable references for building energy analysis methods and supplements in LEED standards.

Due to an increase in energy demands, it has become vital to devise efficient energy policies. Literature has suggested multiple factors influencing the consumption of specific energy types. Among others, institutional quality (INQ) is another factor that can determine energy consumption. Given this, the current study aimed to investigate the impact of INQ on fossil fuel energy (FFE) and renewable energy consumption (REC).

The empirical analysis was conducted on 20 years (2000–2019) of data from South Asian economies, and regression among variables was established by employing the dynamic ordinary least square and fully modified ordinary least square models. The selection of both techniques is subject to the existence of cointegration identified by the Johansen cointegration test. Other pre-estimation techniques include cross-section dependence and unit root testing validating the estimation of coefficients in the long run.

The analysis mainly reveals the negative impact of INQ on FFE and the positive impact of INQ on REC. The authors further find the asymmetric impact of control variables including foreign direct investment inflow, economic growth, inflation rate, financial sector development and energy investment on the consumption of both types of energy.

Given the positive influence of INQ on REC, it is recommended to focus on improving the efficiency of institutions specifically those that are directly linked with energy-related policies. A better INQ can ensure environmental sustainability by enhancing the consumption of renewable energy. Therefore, it is advised to exert more efforts to improve the INQ.

In view of the positive influence of INQ on REC, it is recommended to focus on improving the efficiency of institutions specifically that are directly linked with energy-related policies. A better INQ can ensure environmental sustainability by enhancing the consumption of renewable energy. Therefore, it is advised to exert more efforts for improving the INQ.

This study offers robustness to the empirical findings of existing literature on the INQ-REC nexus and complements the underdeveloped literature on the INQ-FFE relationship.

The purpose of this paper is to explore the developments in final electricity consumption, estimate the portions of changes that can be attributed to national, sectoral or regional factors, and to investigate determinants of the regional component (RC) in Croatia at the subnational level in the period 2001-2013.

In the first stage, the dynamic shift-share method is used to decompose final electricity consumption, and then, in the second stage, the panel population-averaged logit model is conducted to find the main determinants of the extracted RC.

The results show that both the sectoral factor and the regional factor are responsible for an increase in electricity consumption over the period considered, whereby the regional specificities had a larger impact in general. Thereby, the most developed regions, including the tourism-oriented ones, exhibited the largest average increase in electricity consumption mainly due to positive effects of the regional-specific factors, while the negative effects of these factors were mainly responsible for low average rates of changes in electricity consumption in less developed regions.

The results suggest that regional-specific energy conservation programs might be more effective in improving energy efficiency than the sector-oriented ones, as well as that socio-economic and contextual determinants matter when it comes to the probability of having a positive regional effect on the electricity consumption rate.

The paper investigated the determinants of the extracted RC which has not yet been addressed in the energy economics literature.