A bi-objective heuristic approach for green identical parallel machine scheduling

Sustainability-based performance evaluation of hybrid nanofluid assisted machining

The rapid development of urbanization has led to a rapid increase in total energy consumption. The proportion of domestic energy consumption to total energy consumption has gradually increased and has become the major driving force for energy consumption. With the pressure from urbanization and domestic energy consumption, it is necessary to study the impact of urbanization on domestic energy consumption of the regional level and to explore the function paths of these two factors. The findings are helpful to realize sustainable development based on the actual situation analysis, horizontal survey data and statistical yearbook panel data. The current situation and changing trends in domestic energy consumption of Shandong Province are systematically examined through field investigation and survey questionnaire. The time-series econometric model is applied to analyze the relationship between urbanization rate and total domestic energy consumption. The research results show that the total domestic energy consumption and urbanization rate of Shandong are generally increasing. The urbanization rate development and domestic energy consumption have a long-term stable and balanced relationship. An increase in urbanization rate can cause an increase in domestic energy consumption. There is a slight difference between the average total energy consumption levels of urban and rural residents, but their energy consumption structures differ a lot. These findings provide basic data support and reference for local governments to formulate energy- conservation and emission-reduction policies.

Improvements of manufacturability and living standard driven by industrialization and urbanization typically cause a spike in total energy consumption (TEC) and related carbon emissions (TCEM). However, there have been few comparative studies to include industrial and residential energy consumption (IEC and REC, respectively) and related carbon emissions (ICEM and RCEM, respectively). Since China is a major emerging industrial country wherein urbanization is still ongoing, the present study was conducted in an attempt to analyze the temporal evolution of China’s continued energy consumption and related carbon emissions regarding both industrialization and urbanization. The influencing factors of TCEM, RCEM and ICEM are determined via the log-mean divisia index (LMDI) model. The results showed that both TEC and TCEM gradually increased (apart from a slight decrease in 2014); REC and RCEM increased steadily with no sharp peak; while IEC and ICEM declined sharply. TCEM was positively affected by economic output, consumption level, and population size; the influence of consumption level became more and more significant. Per capita GDP and per capita expenditure were the most significant driving factors for RCEM, while industrial added value (IAV) was the main driving factor for ICEM. The temporal evolution and influencing factors of energy consumption and carbon emissions had stage-related characteristics in accordance with Shanghai’s three stages of development. The Shanghai case study provided a comprehensive understanding of energy consumption and related carbon emissions from the dual perspective of industrialization and urbanization.

Amid global climate change and resource constraints, China’s logistics industry faces high energy consumption and carbon emissions, hindering sustainable development. This study analyses the interaction between logistics green total factor productivity and green low-carbon consumption using data from 30 Chinese provinces during 2012–2022. Results show that while logistics energy intensity is decreasing, total energy consumption and carbon emissions are still rising. The concept of green low-carbon consumption is spreading but has limited impact on logistics’ low-carbon development, with regional disparities. Most provinces have high coupling and coordination degrees but no significant spatial clustering.

With the process of economic transformation and structural adjustment, low-carbon development has become an intrinsic and realistic demand for China's national economic and social development. This paper builds a system dynamic (SD) model of China's low-carbon development based on the internal influencing mechanisms of carbon sources, carbon flows, and carbon sinks, and then explores the impact of carbon sources, carbon flows, and carbon sinks system on carbon emissions under different scenarios. The model in our study is effective and practical, and it can not only clearly demonstrate the interaction mechanism between various systems but also provide effective policy recommendations for policymakers. The results show that (1) with the steady growth of the economy, China's total energy consumption and carbon emissions have increased significantly. By 2025, the total energy consumption will reach 628 798 ten thousand tons, and the carbon emissions will reach 689 257 ten thousand tons; (2) as for energy and industrial structure, on the one hand, energy restructuring is more effective in reducing carbon emissions than is industrial restructuring; (3) carbon sinks also play an important role in reducing greenhouse gas emissions.

The relationship between total energy consumption and economic growth has been extensively explored in the past, but relatively few researchers have examined the increasing trend of energy consumption and development policies of a developing country like Pakistan as well as direction of causation of this relationship that remains controversial. The objective of this paper is to empirically investigate four alternatives but equally plausible hypotheses. These are as follows: (i) development factors cause total primary energy consumption, (ii) energy consumption cause development factors, (iii) there is a bidirectional causality between the two variables, and (iv) both variables are causality independent. To investigate the relationship between the two variables set, a time series cointegration and Granger causality tests have been employed separately. Secondary data pertaining to Pakistan from 1980 to 2009 has been used for analysis. The empirical result of causality strongly supports the bidirectional relationship between carbon dioxide emission and energy demand and industrialization and energy demand. There is a unidirectional causality relationship between the energy demand and population growth. However, neither agriculture value addition nor energy demand affect each other.

Against the backdrop of the increasing scarcity of natural mineral aggregates and the underutilization of excessive steel slag resources, extensive research has confirmed the applicability of steel slag as a road aggregate and its favorable pavement performance properties. However, systematic quantitative assessments of steel slag asphalt pavement (SSAP) across multiple dimensions, including environmental and economic benefits, remain relatively limited. Among SSAP with 100% replacement of coarse aggregates by steel slag, the material production stage exhibits the lowest energy consumption and carbon emissions; this is followed by SSAP with a 60% steel slag replacement ratio, while conventional asphalt pavement (CAP) with 0% replacement demonstrates the highest values. When the transportation distance for natural aggregates is 180 km and for steel slag is 60 km, SSAP with 100% steel slag replacement shows the minimal energy consumption and carbon emissions during the transportation stage, whereas CAP with 0% replacement records the highest. Notably, this relationship reverses in the construction stage. The total carbon emissions generated during the construction period for SSAP with steel slag replacement ratios of 0%, 60%, and 100% are 142,841.16 kg, 122,775.15 kg, and 119,220.11 kg, respectively. Specifically, CAP (0% replacement) exhibits the maximum total energy consumption and carbon emissions during construction, while SSAP with 100% steel slag replacement presents the minimum values. These results demonstrate that replacing natural aggregates with steel slag can effectively reduce energy consumption and carbon emissions during the construction of asphalt pavements.

This study focuses on the actual project of rural road ‘concrete to asphalt’ pavement renovation, deeply integrating the design concept of full structure regeneration and the innovation of in-situ regeneration technology for old cement pavement. Through the life cycle assessment method, an environmental impact quantification assessment model is used. Based on the analysis of the environmental benefits of traditional pavement renovation technology (using multi-hammer crushed stone and hot mix asphalt production technology), the environmental advantages of the full-structure regeneration technology scheme are outlined, including the recycling of recycled asphalt pavement materials, application of emulsified asphalt plant mixing cold regeneration technology, and implementation of resonance crushed stone on-site regeneration technology. At the same time, sensitivity analysis methods are used to deeply analyze the sensitivity of changes in parameters such as surface and base thickness, transportation distance, and environmental factors to the overall environmental impact. The research results indicate that the optimization plan can achieve a 57.97 % reduction in total energy consumption and a 71.45 % reduction in total carbon emissions. Additionally, the recycling and utilization of RAP materials significantly reduce the demand for new mineral materials and asphalt. For a 1-kilometer road surface, mineral materials save up to 82.39 % and asphalt saves up to 27.11 %. The energy consumption and carbon emissions generated during the raw material production stage in both schemes contribute the most to the environmental impact of road construction. The analysis found that the environmental benefits of the optimization plan mainly come from the recycling of the old cement concrete pavement as the base layer after resonance crushing, as well as the use of emulsified asphalt mixture cold recycling instead of the lower layer of hot mix asphalt concrete, achieving full-scale recycling of the pavement renovation. The relevant findings provide a data foundation or the improvement and upgrading of energy-saving and emission reduction technologies in road surface renovation and offer valuable insights for future research on sustainable road surface renovation strategies.

The high quality of life in the modern sense is directly related to energy consumption and associated with the provision of «clean» food products and environment, comfortable housing, public and private transport. Increasing the availability of energy for the developing countries allows their residents to live longer and more comfortably. At the same time, in the developed European countries with high quality of life, energy consumption is decreasing due to the implementation of energy efficiency and energy saving policies. The Human Development Index, the world’s best-known and most widely used integrated assessment of quality of life, does not include energy consumption indicators. The aim of our research is to study the relationship between energy consumption and quality of life, and prove the need to consider energy consumption indicators in order to improve the methodology framework for assessing quality of life. Using the method of cluster analysis, 77 countries of the world are grouped according to a set of indicators that characterize income, energy consumption, use of renewable energy sources, and CO2 emissions. As a result, the relationship between the level of human development, which is a universal characteristic of quality of life, and these indicators was identified and evaluated. It has been proved that the most prosperous countries in terms of quality of life and energy use are those in which relatively low indicators of primary energy consumption and CO2 emission are combined with high incomes and human development level. The progressive structure of energy consumption ensures the achievement of a higher quality of living, while high energy consumption is not a sufficient condition for this. Against the background of low levels of total primary energy consumption and GDP per capita, CO2 emissions, as well as a low share of renewable energy in total energy consumption, high quality of life is present mainly in the «new» EU member states, which provide it through rational energy consumption. The average level of human development is inherent in a group of countries of the former USSR (which includes Ukraine), as well as some countries in Latin America, the Middle East and Southeast Asia. At the same time, countries such as China and South Korea are reducing energy consumption owing to technological progress and have a positive dynamics of human development indicators. Instead, low levels of energy consumption in the former Soviet Union and Latin America are due to insufficient sustainability of economic and social development, human development in particular. The obtained results substantiate the need to improve the methodology for assessing the quality of life taking into consideration energy consumption indicators.

Economic growth, energy conservation and emissions reduction: A comparative analysis based on panel data for 8 Asian-Pacific countries

Trade and consumption of energy varieties: Empirical analysis of selected West Africa economies

The electric power consumed by servers has to be reduced in a cluster in order to realize eco society. We take a macro level approach to reducing the total electric energy consumption of servers to perform application processes in a server cluster. Servers are now equipped with multi-core CPUs. In this paper, we discuss a multi-level power consumption (MLPC) model of a server with a multi-core CPU. Here, the power consumption of a server depends on the number of active cores and active threads where at least one application process is performed. We also discuss a multi-level computation (MLC) model which gives the expected execution time of a process which is concurrently performed with other processes on a server with a multi-core CPU. Based on the MLPC model and the MLC model, we discuss an energy-aware (EA) algorithm to select a server for each process requested by a client in a cluster so as to reduce the total electric energy consumption while satisfying deadline requirements of the processes. We evaluate the EA algorithm and show not only the total energy consumption but also the average execution time of each process is reduced in the EA algorithm compared with the round-robin (RR) and random (RD) algorithms.

To better construct an energy consumption model for the asphalt mixture production stage, this study divides the production process into four phases: aggregate dust removal, aggregate drying, asphalt heating, and mixture blending. Utilizing measured data from various regions in Anhui Province, the model considers factors such as season, mixture type, and energy consumption type. The study quantitatively compares and analyzes the energy consumption results obtained from three calculation methods: the theoretical method, the quota method, and the energy consumption model method. The results indicate that the total energy consumption shares of the aggregate dust removal, aggregate drying, asphalt heating, and mixture blending phases are 4.23%, 69.50%, 17.75%, and 8.52%, respectively. The primary energy consumption during the asphalt mixture production stage is concentrated in the aggregate drying and asphalt heating phases. The energy consumption model based on on-site measurements effectively reflects the actual energy consumption levels during the asphalt mixture production stage. Moreover, the total energy consumption calculated by the three methods follows the order quota method > energy consumption model method > theoretical method. By constructing an energy consumption model based on measured data, it is possible to more accurately assess the energy usage during the asphalt mixture production process. This helps optimize production techniques, reduce energy consumption and carbon emissions, and provide a scientific basis for sustainable road construction.

This study was conducted in order to model energy consumption and greenhouse gas emissions for peanut production in Guilan province of Iran using artificial neural network (ANN). Also, the multi-objective genetic algorithm was used for optimization of energy inputs and GHG emissions in the region. Data were randomly collected from 120 farms in Astaneh Ashrafiyeh city with face to face questionnaire method. The results illustrated that the total energy consumption and the average yield were calculated as 19248.04 MJ ha-1 and 3488.39 kg ha -1 , respectively. Moreover, the results showed that the share of chemical fertilizers (mainly nitrogen) and diesel fuel energy to the total energy input were the highest. Also, the energy used efficiency ratio calculated as 4.53. The results of GHG emissions analysis showed the total GHG emissions were 571.18 kgCO2eq. ha -1 and the diesel fuel has the main reasonable of GHG emissions in peanut production. In this study, several direct and indirect factors have been identified to create a model based on ANN to predict energy use and GHG emissions in peanut production. The ANN model with 9-22-2 structure was capable of predicting the peanut yield and GHG emissions. Moreover, the results of the best topology showed that R 2 was 0.994 and 0.999, RMSE was 0.076 and 0.003 and MAPE was 0.174 and 0.009 for peanut yield and GHG emissions, respectively. The results of optimization indicated the total energy consumption and GHG emissions generation was calculated about 6888 MJ ha -1 and 159.08 kgCO2eq. ha -1 , respectively. The total GHG emissions reduction was found to be 412.09 kgCO2eq. ha -1 in optimal generation toward present farms.

Green transportation scheduling with speed control: trade-off between total transportation cost and carbon emission