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As the path of industrial eco-efficiency (IEE) needs to be re-examined from a multi-dimensional perspective, this research opens the internal "black box" of China's industrial eco-economic system driven by sustainable development goals (SDGs) and decomposes IEE into industrial production and sustainable development stages. It further divides the industrial production stage into two parallel subsystems of water use efficiency (WUE) and energy efficiency (EE) and derives SDG6 efficiency (SDG 6 E) and SDG7 efficiency (SDG 7 E) in the sustainable development stage respectively. The paper proposes a meta two-stage parallel entropy dynamic DDF-DEA model to re-measure IEE of 30 provinces in China from 2015 to 2021, discusses the relationship between overall efficiency and stage efficiency, and by using the Tobit model explores various types of influencing factors by subregion. The results show that the modified DDF-DEA model can explore China's IEE more accurately under SDGs. China's IEE is more stable from 2015 to 2021, with the efficiency value always fluctuating between 0.59 and 0.64, the overall level is low, and there is more room for improvement. There are significant inter-provincial differences in IEE and efficiency inertia. In more than 75% of the provinces SDG 7 E constrains IEE improvement, while the dynamic evolution of IEE and its subsystem efficiency varies across regions. Environmental regulation is a common factor affecting IEE across the country and in the east, central, and west regions, whereas the level of economic development, openness to the outside world, and urbanization rate are differential factors affecting IEE across the country and in the three major regions. This study offers new perspectives for exploring the path of IEE enhancement and providing empirical references for the collaborative construction of industrial ecological civilization. [Display omitted] [ABSTRACT FROM AUTHOR]

Increased competition and reduced profit margins are pushing manufacturing plants to find ways to improve operational efficiency and reduce costs. At the same time, government incentives and community initiatives are encouraging the industrial sector to pursue sustainability in operation and energy consumption. This has introduced a paradigm shift where the focus of plant operation is moving from solely cost reduction to one that considers asset management, emission control, and utilization of alternative energy resources. Mathematically speaking, the problem is changing from a single objective optimization to one with multiple objective functions that may at times be contradictory. The focus of this paper is to propose a methodology for management of an industrial plant considering the above factors. It is assumed that the plant is equipped with distributed generation (DG), and can also participate in demand response (DR). Further, the effect of ambient temperature on the lifetime and available capacity of assets such as the plant transformer has been taken into account and incorporated into their daily schedule. The objective is then to find an optimal solution for operating various workstations in such a way that it minimizes the operational costs, the emissions produced, and the loss of life of assets exposed to excess temperatures. A multi-objective optimization problem is therefore formulated through extended lexicographic goal programming, which is further modified to ensure Pareto optimality. A case study has also been presented for a manufacturing plant for luxury vehicle cockpit assembly line. [ABSTRACT FROM AUTHOR]

The aim of Sustainable Development Goal 7 is to ensure access to affordable, reliable, sustainable and modern energy for all. Increased energy efficiency is particularly important in the construction industry because urban agglomerations currently consume up to 80% of global energy, 40% of which is attributable to buildings. In countries like Italy however, where historical buildings constitute a significant part of the building patrimony, improving energy efficiency is not always straightforward as interventions must take into account the historical materials used as well as the context of the construction. Therefore, the aim of this research was to establish a methodology that would identify the most effective means of achieving energy efficiency taking into account the historical aspects of a building and its cultural context. The methodology proposed and described in this paper involves: preliminary analyses to identify the elements of value and the level of transformability of facades; the selection of different insulating materials; dynamic simulations of building behaviour; analyses of intervention costs in relation to each type of insulation and finally the preparation of a multi-criteria matrix to identify the most effective method of improving energy efficiency of a historical wall with differing degrees of transformability. Each insulating material was analysed in relation to heating and cooling requirements and material cost impact. The methodology was applied to a case study in the Province of L'Aquila, Italy. In the paper a multicriteria framework, for the improvements in the thermal efficiency of a building, it is presented. Specifically the attention is focused on heating requirements, cooling requirements and cost impact. The results indicate that, with respect to historical building, it is possible to adopt strategies finalyzed to improve the energy efficiency of these buidings, contributing in this way, also toward the SDG7. Specifically, the best performance for a wall with a high level of transformability can be obtained using calcium silicate, extruded polystyrene, kenaf and hemp, whilst, in the case of a wall with a low level of transformability, the best materials are calcium silicate, polyurethane and linen fiber. Finally, in the case in which external constraints prevent the application of a thick coat and internally there is limited space, the use of calcium silicate was found to be the best energy efficient measure. [ABSTRACT FROM AUTHOR]