[en] Highlights: • Embodied carbon emissions have been calculated under both production principle and consumption principle. • SDA method have been used to examine the driven factors of changes in CO₂ emissions. • CO₂ emissions growth is mainly caused by the expansion of final demand scale and changes in input structure. • Production-based emissions are decomposed into domestic demand emissions and external demand emissions from each area. • Consumption-based emissions are decomposed into domestic and outshore emissions from each region. -- Abstract: To understand the impact of China's internal trade on China's carbon emissions, this article used the multi-regional input-output model to compare embodied carbon emissions based on production principle and consumption principle in the eight major economic regions of China. Besides, the SDA method was used to reveal the drivers of changes in CO₂ emissions. The study uses data from the 2007 and 2012 multi-regional input-output tables. The result shows that domestic demand emissions are the primary source of production-based emissions in China, but the proportion of external demand emissions is increasing rapidly. According to the structural decomposition of the embodied carbon emissions, it can be seen that the carbon emissions caused by the trade in intermediate products have always been a major component of external demand emissions. Further research indicates that the rapid growth in carbon emissions from the production and consumption side of the region is mainly attributed to the expansion of the final demand scale and changes in input structure of the production department. The most critical factor that restrains the increase in carbon emissions on both principles in all regions is the reduction of emission intensity in the production sector. The conclusion of this paper has important implications for how to coordinate inter-provincial trade and regionally balanced development under open economic conditions.

[en] Highlights: • Decomposition method for energy-consumption analysis is applicable. • The scale effect of output has a positive impact on the growth of energy consumption. • Energy intensity effect has a negative impact on the growth of energy consumption. • Production structure effect make China's energy consumption continue to grow. -- Abstract: The principle of input-output analysis, the Mean Rate of Change Index and the Logarithmic Mean Divisia Index were used to conduct the quantitative analysis of the current China's energy consumption from the national industrial level and regional level. It was aimed to calculate the impact and contribution rate of various factors on energy consumption growth in China. The purpose of this research is to analyze the changes of energy consumption of the 30 provinces in China during the period of 2006–2015. The results show that the output scale effect and production structure effect play a leading role in China's energy consumption growth, while China's energy intensity effect can effectively suppress it. The scale effect of output and the energy intensity effect are the main driving factors affecting the growth of China energy consumption. The scale effect of output plays a promoting role of the growth of China energy consumption, and the energy intensity effect can effectively curb the growth of China energy consumption.

[en] We present probability-fidelity tradeoffs for a varying quantum operation with fixed input-output states and for a varying inversion of a fixed quantum operation.

[en] Highlights: • Direct and indirect nexus accounting framework is proposed for urban agglomeration. • The multi-regional input-output analysis and structural path analysis are combined to investigate the energy-water nexus paths in economic system. • The top 50 ranking paths of water-related energy induced by rural household, urban household, and government consumption are investigated. • Sectoral flows within Beijing-Tianjin-Hebei region account for 87% of the total while cross-regional only accounts for 13%. Energy and water are closely intertwined within economic sectors in urban systems. The direct and indirect linkages between them in economic systems, termed the energy–water nexus, have been widely studied from production and consumption perspectives. However, the step-by-step linkages from initial production to final consumption remain unclear, particularly the indirect linkages. In this paper, we develop a multiregional energy–water nexus path model based on multiregional input–output analysis and structural path analysis. The results show that the top 50 ranking paths of water-related energy induced by rural household, urban household, and government consumption account for 84%, 82%, and 90%, respectively, of total flows, whereas the corresponding figures for energy-related water are 57%, 58% and 76%, respectively. The proportion of the top 50 paths within total paths is much lower for energy-related water than is the case for water-related energy. Sectoral flows within Beijing–Tianjin–Hebei account for 87% of the total, whereas cross-regional flows only account for 13%. By comparing the energy–water nexus from production, consumption, and supply chain perspectives, we aim to identify the critical, yet often overlooked, energy–water nexus paths and transmission sectors to enhance coordinated energy–water management in urban systems.

[en] As the world’s top energy consumer, China is facing a great challenge to solve its energy supply issue. In this paper energy use from all industrial sectors in China’s economy of 2007 was explored by conducting an extended environmental input–output analysis. We compare the energy consumption embodied in the final demand for goods and services from 29 sectors with the energy demand required for the actual production process in each sector. Two different viewpoints for sectoral energy use have been presented: energy use is directly allocated to the producer entity, and energy use is reallocated to sector’s supply chain from consumption perspective. Our results show that considerable amount of energy use is embodied in the supply chain, especially for “Construction” and “Other Service Activities” sectors, which is not detected if energy use is allocated on a production basis. When further dividing embodied energy consumption into direct energy consumption and indirect energy consumption, total indirect energy consumption is much higher than that of total direct energy consumption, accounting for 80.6% of total embodied energy consumption in 2007. Our results provide a more holistic picture on sectoral energy consumption and therefore can help decision-makers make more appropriate policies. - Highlights: ► A hybrid IO-LCA model was employed to analyze China’s energy use at sectoral level. ► A case study on China’s sectoral energy consumption is done. ► Construction and service sectors are actually energy intensive from the supply chain perspectives. ► Upstream and downstream ectoral collaboration along the whole supply chain is necessary. ► Energy conservation policies should be based upon a comprehensive analysis on sectoral energy use.

[en] An ambitious shale gas extraction plan has been proposed. The huge investment of shale gas may put an effect on the whole China’s economy, especially for employment. However, there is few study to date has quantified these effects. The aim of this paper is to quantify these effects especially employment creation and figures out whether shale gas investment in China is a good choice or not. Input-output analysis has been utilized in this study to estimate the employment creation in four different Chinese regions. Our findings show that shale gas investment will result in creating 660000, 370000, 140000 and 58000 equivalent jobs in Sichuan, Chongqing, Inner Mongolia and Guizhou, respectively. Considering the potential risks of environmental issues, we suggest that it may be a better strategy for the government, at least in the current situation, to slow down shale gas development investment. (paper)

[en] Highlights: • The relationship between industrial driving effect and energy intensity is studied. • Panel quantile regression is adopted in the analysis. • The industrial driving effects of various countries are changing over time. • The roles played by countries have different impacts on energy intensity. • Industrial driving effect should be considered for trade policies. As the industrial structure adjusts and driving effects change in different countries, the regional pattern of industrial driving effects is reshaped, and the new pattern is bound to lead to changes in energy consumption. Therefore, this paper uses multiregional input-output analysis, complex network theory, and panel quantile regression model to explore the impact of the topological structure of the global industrial driving network on energy intensity. The empirical results show that China’s ability to drive economic activity is becoming stronger, but its level of dependence is also increasing. The industrial dependence of Russia is also relatively high, but over time it has been gradually decreasing. Countries with high energy intensity should reduce their role in driving others and rely less on external resources. For countries with low energy intensity, they could conduct trade cooperation with more countries, but the driving effect strength with other countries cannot be too high. Understanding the relationship between industrial driving effects and energy intensity is of great significance for the government in the implementation of trade policies and industry policies, which have a large impact on energy intensity.

[en] A complex system of production links our greenhouse gas emissions to our consumer demands. Whilst progress may be made in improving efficiency, other changes in the production structure may easily annul global improvements. Utilising a structural decomposition analysis, a comparative-static technique of input-output analysis, over a time period of around 30 years, net greenhouse emissions are decomposed in this study into the effects, due to changes in industrial efficiency, forward linkages, inter-industry structure, backward linkages, type of final demand, cause of final demand, population affluence, population size, and mix and level of exports. Historically, significant competing forces at both the whole of economy and industrial scale have been mitigating potential improvements. Key sectors and structural influences are identified that have historically shown the greatest potential for change, and would likely have the greatest net impact. Results clearly reinforce that the current dichotomy of growth and exports are the key problems in need of address.

[en] Highlights: • Research pertaining to system reliability optimization has been categorized chronologically into three eras. • The first era, the era of mathematical programming involved rigorous optimization methods, yet the problems are not always realistic. • The second era, the era of pragmatism, involved expanding the problem domain to include a broader range of problems, and more realistic problems. • The final and current era, the era of active reliability improvement involves dynamic reliability optimization models responding to changing conditions and data. • System reliability optimization problems remain challenging, but important, while both the problems and corresponding solution methods evolve. -- Abstract: System reliability optimization is a living problem, with solutions methodologies that have evolved with the advancements of mathematics, development of new engineering technology, and changes in management perspectives. In this paper, we consider the different types of system reliability optimization problems, including as examples, the redundancy allocation problem (RAP), the reliability allocation problem and the reliability-redundancy allocation problem (RRAP), and provide a flow of discussion and analysis on the evolution of the approaches for their solutions. We consider the development and advancement in the fields of operations research and optimization theory, which have allowed the formalization and continuous improvement of the methods and techniques to address reliability design problems of even very complex systems in different technological domains. Technological advances have naturally brought changes of perspectives in response to the needs, interests and priorities of the practical engineering world. The flow is organized in a structure of successive “Eras of Evolution,” namely the Era of Mathematical Programming, the Era of Pragmatism, the Era of Active Reliability Improvement. Insights, challenges and opportunities are highlighted.

[en] In the present study, the effect of the laws and regulations of the Institute of Standards and Industrial Research of Iran (ISIRI) in four industry sectors including Steel and Iron, Tile, Ceramic, Cement, and Brick on the life cycle embodied energy of buildings was investigated. Globally, 20.1% of the total world annual final energy consumption is found in the building sector consists of residential and commercial users. Energy consumption in buildings is found in different forms, including the production of primary materials, their transportation to the building site, construction, operation, renovation, maintenance, and demolition. In this research, different solutions were considered, and hybrid input-output analysis was chosen as an effective method to find primary embodied energy in buildings. The embodied energy of four high-consuming sectors in Iran was substituted with input-output data to increase the reliability of the study. The embodied energy of the building in the present case study is 10.71 GJ/m² in 25 years of usage. Using the laws and regulations of ISIRI in 2006, which reports the standard energy use in four main industries in Iran, the embodied energy is reduced to 9.15 GJ/m² in 25 years of usage, which shows that a significant amount of energy (1911 GJ primary energy) and greenhouse gases (141.2 kg GHGs) could be saved in the case study’s building. Further data collection in other industry sectors like aluminum, glass, and paint will change the results and increase the reliability of the research.