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A balance between industrial pollution and economic growth becomes a major policy issue to attain a sustainable society in the world. To discuss the problem from economics and business perspectives, this study proposes a new use of DEA (Data Envelopment Analysis) as a methodology for unified (operational and environmental) assessment. A unique feature of the proposed approach is that it separates outputs into desirable and undesirable categories. Such separation is important because energy industries usually produce both desirable and undesirable outputs. This study discusses how to unify the two types of outputs under natural and managerial disposability. The proposed DEA approach evaluates various organizations by the three efficiency measures such as OE (Operational Efficiency), UEN (Unified Efficiency under Natural disposability) and UENM (Unified Efficiency under Natural and Managerial disposability). An important feature of UENM is that it separates inputs into two categories and unifies them under the two disposability concepts in addition to the proposed output separation and unification. This study incorporates an amount of capital assets for technology innovation, as one of the two input group, into the measurement of UENM. Then, it compares UENM with the other two efficiency measures. This study is the first research effort in which DEA has an analytical capability to quantify the importance of investment on capital assets for technology innovation. To confirm the practicality of the proposed approach, this study applies the three efficiency measures to a data set regarding manufacturing and non-manufacturing industries of 47 prefectures in Japan. This study empirically confirms the validity of Porter hypothesis in Japanese manufacturing industries, so implying that environmental regulation has been effective for betterment on the performance of Japanese manufacturing industries. Another important finding is that the emission of greenhouse gases is a main source of unified inefficiency in the two groups of industries. Therefore, Japanese industries, examined in this study, need to make their efforts to reduce the greenhouse gas emissions and air pollution substances by investing in capital assets for technology innovation.

A balance between industrial pollution and economic growth becomes a major policy issue to attain a sustainable society in the world. To discuss the problem from economics and business perspectives, this study proposes a new use of DEA (Data Envelopment Analysis) as a methodology for unified (operational and environmental) assessment. A unique feature of the proposed approach is that it separates outputs into desirable and undesirable categories. Such separation is important because energy industries usually produce both desirable and undesirable outputs. This study discusses how to unify the two types of outputs under natural and managerial disposability. The proposed DEA approach evaluates various organizations by the three efficiency measures such as OE (Operational Efficiency), UEN (Unified Efficiency under Natural disposability) and UENM (Unified Efficiency under Natural and Managerial disposability). An important feature of UENM is that it separates inputs into two categories and unifies them under the two disposability concepts in addition to the proposed output separation and unification. This study incorporates an amount of capital assets for technology innovation, as one of the two input group, into the measurement of UENM. Then, it compares UENM with the other two efficiency measures. This study is the first research effort in which DEA has an analytical capability to quantify the importance of investment on capital assets for technology innovation. To confirm the practicality of the proposed approach, this study applies the three efficiency measures to a data set regarding manufacturing and non-manufacturing industries of 47 prefectures in Japan. This study empirically confirms the validity of Porter hypothesis in Japanese manufacturing industries, so implying that environmental regulation has been effective for betterment on the performance of Japanese manufacturing industries. Another important finding is that the emission of greenhouse gases is a main source of unified inefficiency in the two groups of industries. Therefore, Japanese industries, examined in this study, need to make their efforts to reduce the greenhouse gas emissions and air pollution substances by investing in capital assets for technology innovation.

Abstract Recovery of waste heat in large industrial plants is nowadays an important topic of thermal optimization. In the present study, energy, exergy, and exergoeconomic analysis of an integrated system, Tehran's waste-to-energy power plant coupled with an organic Rankine cycle (ORC), is analyzed. Parametric study of essential parameters (moisture content, pinch point temperature differences of the HROG, steam generator's superheat temperature difference, and steam turbine inlet pressure) is performed thermodynamically. The best system performance achieved using R123 as the working fluid of ORC. After implementation of the waste-heat-recovery system into the WtE plant, with R123 the energy and exergy efficiencies increase from 17.27% to 19.51% and 14.49%–16.36%, respectively. Exergy analysis reveals that the gasifier and steam generator are the main source of exergy destruction in the overall system. Additionally, the results of single-objective optimization based on maximum exergy efficiency and minimum total product unit cost were calculated. Furthermore, multi-objective optimization based on genetic algorithm using MATLAB software is implemented to find the optimum point with respect to exergy efficiency and total product unit cost as the objective functions. The exergy efficiency and total product unit cost at the optimum point, considering multi-objective optimization, are 19.61% and 24.65 $/GJ, respectively.

Abstract Recovery of waste heat in large industrial plants is nowadays an important topic of thermal optimization. In the present study, energy, exergy, and exergoeconomic analysis of an integrated system, Tehran's waste-to-energy power plant coupled with an organic Rankine cycle (ORC), is analyzed. Parametric study of essential parameters (moisture content, pinch point temperature differences of the HROG, steam generator's superheat temperature difference, and steam turbine inlet pressure) is performed thermodynamically. The best system performance achieved using R123 as the working fluid of ORC. After implementation of the waste-heat-recovery system into the WtE plant, with R123 the energy and exergy efficiencies increase from 17.27% to 19.51% and 14.49%–16.36%, respectively. Exergy analysis reveals that the gasifier and steam generator are the main source of exergy destruction in the overall system. Additionally, the results of single-objective optimization based on maximum exergy efficiency and minimum total product unit cost were calculated. Furthermore, multi-objective optimization based on genetic algorithm using MATLAB software is implemented to find the optimum point with respect to exergy efficiency and total product unit cost as the objective functions. The exergy efficiency and total product unit cost at the optimum point, considering multi-objective optimization, are 19.61% and 24.65 $/GJ, respectively.

Abstract Chemists, biologists, ecologists, and engineers have been developing a data-base to identify and evaluate risks to humans and the environment and strategies to minimize potential risks from large-scale coal liquefaction. Coal-liquids produced by various processes and under various stages of design and operating conditions have been screened for potential health and environmental effects. Toxicologically active materials have been fractionated and chemical constituents of biologically active fractions have been identified, and the environmental fate of problematic agents is being determined. Results indicate that coal-derived liquids are generally more toxicologically active than shale oil and petroleum crudes. Bioactive agents include primary aromatic amines (PAA), polynuclear aromatic hydrocarbons (PAH), phenolics, and others. Some components of coal-derived materials are taken up by biota and metabolized. Hydrotreating reduces PAA, PAH, and phenol content, as well as toxicological response to coal-liquids. Selective distillation restricts PAA and PAH content, and mutagenicity and carcinogenicity to high-boiling-range materials. Other process conditions and environmental factors also influence chemical characteristics and toxicological activity of coal-derived liquids. Recent findings indicate that biological responses to a given coal-derived liquid component vary, depending on whether that material is presented to the organism or environment as a pure compound or in a complex mixture. The data-base described provides input for assessment and has been used by developers when selecting process modifications and product slates that minimize risk to humans and the environment. These data have also been used in developing occupational health and industrial hygiene practices and may aid in selection of control technologies, mitigative strategies, special handling and accident prevention procedures, or spill-clean-up options to enhance the environmental acceptability of a coal liquefaction industry.

Abstract Chemists, biologists, ecologists, and engineers have been developing a data-base to identify and evaluate risks to humans and the environment and strategies to minimize potential risks from large-scale coal liquefaction. Coal-liquids produced by various processes and under various stages of design and operating conditions have been screened for potential health and environmental effects. Toxicologically active materials have been fractionated and chemical constituents of biologically active fractions have been identified, and the environmental fate of problematic agents is being determined. Results indicate that coal-derived liquids are generally more toxicologically active than shale oil and petroleum crudes. Bioactive agents include primary aromatic amines (PAA), polynuclear aromatic hydrocarbons (PAH), phenolics, and others. Some components of coal-derived materials are taken up by biota and metabolized. Hydrotreating reduces PAA, PAH, and phenol content, as well as toxicological response to coal-liquids. Selective distillation restricts PAA and PAH content, and mutagenicity and carcinogenicity to high-boiling-range materials. Other process conditions and environmental factors also influence chemical characteristics and toxicological activity of coal-derived liquids. Recent findings indicate that biological responses to a given coal-derived liquid component vary, depending on whether that material is presented to the organism or environment as a pure compound or in a complex mixture. The data-base described provides input for assessment and has been used by developers when selecting process modifications and product slates that minimize risk to humans and the environment. These data have also been used in developing occupational health and industrial hygiene practices and may aid in selection of control technologies, mitigative strategies, special handling and accident prevention procedures, or spill-clean-up options to enhance the environmental acceptability of a coal liquefaction industry.

Abstract This paper examines the relationship between hydroelectricity consumption and economic growth in China, while controlling for fossil fuel consumption, financial development, capital, institutional quality and globalization and its components for the period, 1970–2014. We have employed the Bayer and Hanck, (2013) combined cointegration test to examine the long-run relationships between those variables as well as the autoregressive distributed lag method with structural breaks as a robustness check. The empirical findings demonstrate a long-run relationship between those variables. Hydroelectricity consumption, fossil fuel consumption, capital, financial development and globalization and its components have a positive influence on GDP in China. The findings also provide predominant evidence on the long-run feedback hypothesis between the variables. The findings suggest that policies should be implemented to increase the role hydropower in the energy mix for sustainable economic growth in the country.

Abstract This paper examines the relationship between hydroelectricity consumption and economic growth in China, while controlling for fossil fuel consumption, financial development, capital, institutional quality and globalization and its components for the period, 1970–2014. We have employed the Bayer and Hanck, (2013) combined cointegration test to examine the long-run relationships between those variables as well as the autoregressive distributed lag method with structural breaks as a robustness check. The empirical findings demonstrate a long-run relationship between those variables. Hydroelectricity consumption, fossil fuel consumption, capital, financial development and globalization and its components have a positive influence on GDP in China. The findings also provide predominant evidence on the long-run feedback hypothesis between the variables. The findings suggest that policies should be implemented to increase the role hydropower in the energy mix for sustainable economic growth in the country.

Abstract This paper presents an optimal building energy management strategy for the demand response of multi-thermal-zone buildings in the smart electricity grid environment. The proposed method includes a machine learning model, based on a neural network, for a building heating ventilation and air conditioning system. The learned model is then applied to an optimization problem to determine the optimal management scheduling of building loads. The goal of the optimization problem is to minimize building electricity costs and reduce the overall building energy consumption during peak load hours while satisfying human comfort demand. To overcome the coupling issue between the building internal-heat-gain loads and the building heating ventilation and air conditioning system, an iterative algorithm is proposed to solve the optimization problem. In each iteration, a mixed-integer linear programming technique is used to solve a sub-optimization problem for the building internal-heat-gain loads and its results are then applied to another sub-optimization problem, solved by using a particle swarm technique, for the building heating ventilation and air conditioning system. The iterative optimization algorithm stops when convergence between the optimization for the building heating ventilation and air conditioning system and the optimization for the building internal-heat-gain loads is properly reached. EnergyPlus is used to build and simulate complex buildings with multiple-thermal zones according to real-life conditions. The simulation model is also used to test and evaluate the effectiveness of the proposed machine-learning model and the iterative optimization algorithm and the improvement of building energy management in terms of energy consumption efficiency, cost saving, and satisfaction of human comfort.