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The carbon trading market has introduced new opportunities for the low-carbon transformation of China's power industry. To realize real-time coupling of the electric–carbon market at the mechanism level, this study constructs a P2P trading mode that improves economic and environmental benefits through innovative mechanisms. First, we propose a novel indicator, Green Energy, which has multidimensional composite attributes and accurately characterizes the supply of clean and low-carbon energy. Green Energy is used as a matchmaking indicator in the P2P electricity market to increase the rate of renewable energy consumption and encourage emission reduction of fossil energy suppliers. Second, the carbon price is dynamically adjusted according to real-time electricity consumption information and it is transmitted to the electricity market through Green Energy. Third, blockchain cross-chain interoperability technology is introduced to establish a real-time data-sharing network for electricity and carbon co-trading. Moreover, a case study is conducted, and the simulation results show that the proposed method improves social welfare by over 18% compared to the traditional trading mode. And it can improve renewable energy utilization efficiency by 26.78% and reduce carbon emissions by more than 17%. This study provides a reference for the real-time coupled electricity and carbon markets. • A novel indicator Green Energy for real-time coupled electricity and carbon markets is proposed. • Green Energy characterizes the clean and low-carbon attributes of different energy supplies. • Electricity and carbon co-trading mode is designed to improve economic and environmental benefit. • Blockchain cross-chain interoperability technology promotes the implementation of electricity and carbon co-trading. [ABSTRACT FROM AUTHOR]

The growing emphasis in alternative revenue streams, from the conversion of sugarcane bagasse into energy co-products has prompted a desire by the sugar factories to achieve higher levels of energy efficiency. This study presents a comprehensive overview of the parameters whose steady-state deviation leads to excess energy use in sugar mills. The effect of eight operating parameters on the defined energy indicators was investigated, using a MATLAB simulation of a 250-tonne per hour sugar mill. The captured energy trends were used to develop energy prediction models based on the parameters whose steady-state offsets resulted in excess energy use. High prediction accuracies of over 95% were obtained when the developed energy prediction models were validated for seasonal cane quality and evaporator heat transfer coefficient variations. Increased A-massecuite recycling had a more pronounced effect on the energy used per kilogramme of water in the evaporator unit as compared to the crystallisation unit. Of the eight parameters, elevated imbibition water use and A-massecuite recycling had a cumulative percentage effect of 79% on the overall steam used per tonne of cane and sugar produced. Hence increasing the syrup concentration, decreasing imbibition water use and massecuite recycling are preferred operational strategies for improved energy-efficiency in a sugar mill. Overall, this study strengthens the existing literature by illustrating an approach for developing suitable energy indicators for excess energy use monitoring in sugarcane mills. • Analysis of the sugar mill energy aspects during process transients. • Explores the correlation between energy consumption and the key energy drivers. • Three energy indicators for energy monitoring and benchmarking were developed. • Rise in pan steam demand was mainly driven by the increase in massecuite recycling. • Imbibition water had the highest influence on the overall factory steam consumption. [ABSTRACT FROM AUTHOR]

Abstract: Environmental indicators have been constructed and used as a tool for measuring environmental performance for various purposes in the industrial environment including that of decision-making. However, few studies in the literature address the issue of the quality of the data used for the construction of these indicators, and one of the factors neglected is the uncertainty associated with them. This paper deals with a case study on the uncertainty in the energy indicators of a petrochemical industry in Brazil. In this study, there is an examination of the components involved in making the calculation of energy indicators, as well as their uncertainties. The end result is the development of a methodology that can be used for assessing the uncertainty of the indicators. The evaluation of this uncertainty shows a value greater than appropriate for the intended use of the indicator. In order to reduce this uncertainty, an attempt has been made to point out the most striking features that require investment in human and economic resources, such as improvement in the measurements and implementation of new measurers at critical points. [Copyright &y& Elsevier]

Abstract: Forecasts of energy demand, the fuel mix meeting that demand and the associated emissions are a key requirement for informed energy planning and policy decisions to ensure energy security and address climate change. While there have been many studies on China focusing on the short and medium term (to 2020 and 2050) there is little in the literature focusing on the long term (to 2100). This paper seeks to address those gaps on sectoral energy demands and emissions on long term by following a two-stage approach. It develops key energy indicators on useful energy demand, transport mobility and end use fuel demand for various sectors. The main drivers of these indicators are socio-economic parameters. The indicators are used to project energy service demands and emissions forward for China in TIMES G5 model at least cost approach. The results from this reference scenario suggest that China will require approximately 4 Gtoe of primary energy, by the end of the 21st century to deliver 3 Gtoe final energy consumption, 10 PWh of electricity generation, 1.3 Gtoe of energy imports, which will results in 10 Gt CO2 emissions. [ABSTRACT FROM AUTHOR]

Abstract: As Taiwan has a dense population and only limited natural resources, the government began actively establishing a Taiwan''s sustainable development indicators (TSDI) system in 2003, which was developed with reference to the Pressure–State Response (PSR) framework to evaluate the progress towards sustainability. Energy is a very important factor in leading to sustainable development at the national level. However, there are only two indicators concerning the energy sustainability in the TSDI system. This paper summarizes the current status of energy supply, energy consumption, carbon dioxide (CO2) emissions and renewable energy production since the year 2000. The updated information on the TSDI was also addressed and analyzed to imply the progress towards energy sustainability during 2000–2008. Finally, the weighted-sum method was proposed to make an analysis of energy sustainability from Taiwan''s renewable energy production, showing that this country has experienced a significant progress toward energy sustainability in recent years. [Copyright &y& Elsevier]

Abstract: Energy use as a single indicator or in a set of few indicators is often used in applied research in the building, transportation and energy sectors. However, the environmental relevance of energy indicators is seldom questioned. The relation between environmental relevance and energy indicators might seem obvious. Nevertheless, how this is obvious has not been thoroughly discussed. The aim of this paper is to investigate the environmental relevance of the energy indicator and discuss implications for its use. The approach is to express environmental pressure in different environmental impact categories and determine the contribution to these from energy use. Because not all impact categories are closely linked to energy indicators, the aim and context in which it is used must be apparent. [Copyright &y& Elsevier]

This paper develops a new practical framework of high-cycle fatigue (HCF) probability analysis of local welded plate joints considering the uncertainty in Arctic areas. The framework includes 1) a meta-model of the local energy concentration factor relying on the genetic algorithm and neural network (GA-NN) approach, 2) the identification of uncertainties in the fatigue assessment procedure, and 3) the probability analysis relying on the Monte Carlo simulation with Latin hypercube sampling method. The GA-NN determines the interactive relationship among the geometrical properties for a good estimation of the energy-based indicator concentration factor. The probability analysis considers the uncertainty caused by the meat-model, the local welding geometry, the Young modulus under different temperatures, and the combination of the ice and wave loadings. To determine the uncertainties involved in probability analysis, this study conducts a series of experimental measurements and high-cycle fatigue tests of cruciform welded plate joints. Furthermore, this study discusses the effect of the ice loading, maximum wave stress, and main plate thickness of welded plate joints on the expected fatigue life. The additional ice loading on the welded plate joints can almost cut the expected fatigue life in 33% in comparison with that only under wave loadings, implying the necessity of considering ice loading in Arctic areas. This study also proposes a probability S-N curve with the cumulative probability of 50% based on the maximum local energy-based indicator to estimate the expected fatigue life. Unlabelled Image • A hybrid approach is proposed to assess fatigue life of welded plate joints in arctic areas. • Interactive relationships between geometrical and material properties are determined. • Experimental high-cycle fatigue tests under low temperature are performed. • Uncertainties in local weld geometry, material properties, and S - N curve are considered. • Benchmark study for probabilistic fatigue life of welded plate joints in Arctic areas is conducted. [ABSTRACT FROM AUTHOR]