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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.

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 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.

Abstract In this work, the possibility of steel slag as an effective and low-cost catalyst for the decomposition of biomass pyrolysis tar has been explored based on the high content of iron oxides for sustainable syngas production from biomass. By simple calcination treatment at 800 °C, the loose structure of the steel slag was formed with the main chemical composition of Fe2O3 and MgFe2O4. The steel slag exhibited good catalytic activity on the cracking of biomass pyrolysis tar, and even higher tar conversion efficiency can be obtained by reusing the steel slag, leading to the increase in syngas yield. The presence of additional steam can further promote the tar reforming reactions, leading to the significant increase in H2 and CO. At 800 °C, the tar conversion efficiency reached 94.1% with a high gas yield of 493.5 mL/g. The interaction between steel slag and reductive gases resulted in the reduction of iron oxides into Fe3O4, and more pores were formed for the spent steel slag, which can enhance the contact between active sites and reactants. These characteristics indicate that steel slag has the potential to be used as an efficient catalyst with excellent stability in the long-term biomass tar removal applications.

Abstract Global generation of municipal solid waste (MSW) is predicted to reach over 2.2 billion tons/year in 2025. Landfilling and incineration, the two most common conventional techniques for MSW processing, negatively impact public health. This study developed and demonstrated electricity generation by co-gasification of two underutilized resources: MSW and agricultural biomass. A patented design of 60-kW downdraft gasifier and an internal combustion engine with 10 kW generator were used to generate electricity from co-gasification of various ratios of MSW and biomass. The maximum heating values (LHV) of syngas obtained at MSW ratio of 0, 20, and 40 wt.% were 6.91, 7.74, and 6.78 MJ/Nm3, respectively. At all MSW to biomass ratios, the maximum electric load generated was 5 kW, with electrical efficiencies of 22, 20, and 19.5% at MSW ratios of 0, 20, and 40 wt.%, respectively. The engine CO, NOx, SO2, and CO2 emission decreased with increasing load, while HC emission increased with increasing load. CO, NOx, and CO2 emissions decreased, while HC and SO2 emissions increased with increase in MSW ratio. Thus, the co-gasification system provides a basis for future development of small-scale power generation to utilize local wastes.

Abstract Integrated, model-based energy planning particularly in cities and territories involves different planning and modelling activities, which, from a methodological point of view, can be divided into four phases. The analysis and findings of this study focus on planning “phase I”, which is devoted to preparation and orientation. Despite the importance of this planning phase, which is underlined in several papers, only a few studies have addressed planning phase I partially using a systematic methodology. A brief review of planning activities, problems and methods enables mapping the applicability of these methods to their purpose in planning context. The review reveals that no methodological support is provided to fulfil all of the requirements and tasks of this phase. Thus, a methodology for supporting “phase I” activities is presented and illustrated using Singapore as a case study. The methodology combines methods that are either already used in energy planning or borrowed from the area of inventive problem solving, and a specially developed method. The methodology can explicitly reveal problems, key and hidden contradictions, which allows a better understanding of the situation and requirements for the next planning phase especially when looking for solutions beyond common optimality (innovative solutions).

Abstract Advancedd distribution management systems (ADMSs) integrate distributed generation (DG) units and battery energy storage systems (BESSs), and they are a promising solution to reduce the greenhouse gas emissions in the energy sector. This study addresses the sustainable ADMS (SADMS) design problem by determining the power flow optimal economic dispatches from traditional power plants, DG units, and BESSs to residential areas while maximizing the total profit. The designed SADMS provides a demand response programs with various energy pricing schemes that correspond to different customers and energy consumption loads. Three carbon emission policies (carbon tax, carbon cap, and carbon trade) are considered in the model. An advancedd two-phase (ATP) approach is proposed to solve the described problem. In the first phase, an artificial neuro-fuzzy system (ANFS) is developed based on self-learning and self-adjusting processes to determine the customer demand response loads and DG unit output energies in uncertain environments. A combined ANFS and optimization solver is proposed in the second phase to determine the optimal SADMS economic dispatch. The application of the proposed approach is examined using an empirical case study in Taiwan. The results demonstrate that the proposed ATP approach can determine the optimal economic dispatch with an extremely small deviation in demand response load of 0.92%. In addition, our approach increases the total profit (improving total profit by approximately 1.1%, 0.8%, and 1.9% for the three different carbon emission policy objective functions) and reduces the computational time (by 3.0–6.0 times) compared to those of the genetic algorithm. Finally, the proposed model illustrates that carbon trade is the best policy for improving the total SADMS profit while satisfying the given environmental constraints.

Abstract Evidence on global climate change, being caused primarily by rising levels of greenhouse gases in the atmosphere, is perceived as fairly conclusive. It is generally attributed to the enhanced greenhouse effect, resulting from higher levels of trapped heat radiation by increasing atmospheric concentrations of gases such as CO 2 (carbon dioxide). Much of these gases originate from power plants and fossil fuel combustion. However, the fate of vast amounts of waste heat rejected into the environment has evaded serious scholarly research. While 1 kWh electricity generation in a typical condensing coal-fired power plant emits around 1 kg of CO 2 , it also puts about 2 kWh energy into the environment as low grade heat. For nuclear (fission) electricity the waste heat release per kWh is somewhat higher despite much lower CO 2 releases. This paper evaluates the impact of waste heat rejection combined with CO 2 emissions using Finland and California as case examples. The immediate effects of waste heat release from power production and radiative forcing by CO 2 are shown to be similar. However, the long-term (hundred years) global warming by CO 2 -caused radiative forcing is about twenty-five times stronger than the immediate effects, being responsible for around 92% of the heat-up caused by electricity production.