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Abstract This study investigates the non-linear relationship between urbanization paths and CO2 emissions in selected South, South-East, and East Asian countries over the period 1971–2014. Based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework, we applied the advanced and robust methods of dynamic seemingly unrelated regression (DSUR), dynamic OLS (DOLS), and fully modified OLS (FMOLS) to estimate the long-term effects. The empirical findings revealed the inverted U-shaped effects of urbanization and urban agglomeration and the U-shaped impact of the largest city ratio on CO2 emissions. Urbanization and urban agglomerations improve environmental quality in the long-run and support ecological modernization theory. However, excessive concentration in the largest cities have severely affected the environmental quality and violates the notion of compact-city efficiencies. Moreover, energy intensity and economic growth positively affect CO2 emissions, while trade openness negatively influences CO2 emissions. Our robustness analysis at the country-level applies the augmented mean group (AMG) panel ARDL technique, which further supports the non-linear effect of urbanization paths on CO2 emissions except for a few countries. The results of the panel Granger non-causality approach unveil bidirectional causality of energy efficiency, economic growth, urbanization, and largest city ratio with CO2 emissions. In contrast, unidirectional causality runs from urban agglomeration to CO2 emissions. Our findings have important policy implications as we suggest green urban infrastructures, eco-friendly dwellings, smart cities, country-specific trade policies, and renewable energy options to improve the environmental quality.

Abstract A great deal of heat is wasted in intensive public shower facilities, such as those in schools, barracks and natatoriums, which open up at specified time. It will contribute a lot to energy saving and environmental protection with significant economic benefits to recycle the exhaust heat. In this paper, we propose two different kinds of heat pumps (an electric heat pump and an absorption heat pump) in the heat recovery systems. In both system, the used shower water is drained through a pipe and collected in a gray water pool. When the wastewater reaches certain volume, the heat pump system will begin working and recycling heat. The wastewater is filtered and piped to the heat exchanger to exchange heat with the tap water whose temperature will increase from 12 °C to 25 °C with the wastewater temperature dropping from 30 °C to 17 °C. Then the wastewater is piped to the heat pump evaporator and the tap water is piped to the condenser for farther heating. According to the different characteristics of the electric heat pump and absorption heat pump, we also introduce the processes and control methods of different heat recovery systems in details in this paper. Based on a practical example, this paper analyzes and compares the economic and environmental benefits of three retrofitting schemes, including “exhaust heat recovery using electric heat pump”, “exhaust heat recovery using electric heat pump + gas boiler” and “exhaust heat recovery using direct-fired heat pump”. Then we find out that the heat recovery system using direct-fired absorption heat pump has lower energy consumption, less pollution, lower operating cost, and shorter payback period. And it has a promising practical application.

Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

Abstract As a burgeoning theoretical framework, energy justice has been mostly focused on the energy transition in Western countries, where socio-political settings are largely featured by liberalism and democracy, leaving an obvious gap in its application in other socio-political contexts. As a major energy consumer and a leader of the global low-carbon transition, China is characterized by a distinctive socio-political regime. An array of grand strategies to transform its coal-dominant energy structure have been initiated to ameliorate deteriorating environmental crises in particular and materialize a low-carbon transition in general. Based on extensive evidence, this article incorporates the energy justice framework into the analysis of an ongoing energy transition project in rural Northern China. It contributes to the related research in three dimensions. First, empirically, it demonstrates that the coal-to-gas heating transition project has been swamped with social injustices; the absence of measures to address these would lead this mega-project to profound failure. Second, theoretically, it illustrates that the concerns of justice are even more paramount in an authoritarian context where policy processes are characterized by strong political-administrative intervention and the pursuit of efficiency at all cost. In light of this, it stresses the indispensable role of restorative justice as a core tenet in achieving energy justice in authoritarian socio-political contexts, such as China. Third, this study advocates expanding the evaluation parameters of authoritarian environmentalism to include social consequences.

According to the renewable energy directive 2009/28/EC, the European Union Member States should increase by 2020 the use of renewable energy to 20% of gross final energy consumption and to reach a mandatory share of 10% renewable energy in the transport sector. This study aims to quantify the impact of 2020 bioenergy targets on the land use in the EU, based on the projections of the National Renewable Action Plans in four scenarios: Scenario 1. Bioenergy targets according to NREAPs; Scenario 2. Bioenergy targets according to NREAPs, no second generation biofuels; Scenario 3. Bioenergy targets according to NREAPs, reduced import of biofuels and bioliquids; Scenario 4. Bioenergy targets according to NREAPs, high imports of biofuels and bioliquids. This study also considers the credit for co-products generated from biofuel production. The analysis reveals that the land used in the EU for bioenergy would range between 13.5 Mha and 25.2 Mha in 2020. This represent between 12.2% and 22.5% of the total arable land used and 7.3% and 13.5% of the Utilised Agricultural Area (UAA). In the NREAPS scenario, about 17.4 Mha would be used for bioenergy production, representing 15.7% of arable land and 9.4% of UAA. The increased demand from biofuels would lead to an increased generation of co-products, replacing conventional fodder for animal feed. Considering the co-products, the land used for bioenergy would range between 8.8 Mha and 15.0 Mha in 2020 in the various scenarios. This represent between 7.9% and 13.3% of the total arable land used in the EU and 4.7% and 8.0% of the UAA. In the NREAPS scenario, when co-products are considered, about 10.3 Mha would be used for biofuels, bioliquids and bioenergy production, representing 9.3% of arable land and 5.6% of agricultural land. This study further provides detailed data on the impact on land use in each Member State.

Microalgae have tremendous potential for producing liquid renewable fuel. Many methods for converting microalgae to biofuel have been proposed; however, an economical and energetically feasible route for algal fuel production is yet to be found. This paper presents a review on the comparison of the most promising conversion pathways of microalgae to liquid fuel: hydrothermal liquefaction (HTL), wet extraction and non-destructive extraction. The comparison is based on important assessment parameters of product quality and yield, nutrient recovery, GHG emissions, energy and the cost associated with the production of fuel from microalgae, in order to better understand the pros and cons of each method. It was found that the HTL pathway produces more oil than the wet extraction pathway; however, higher concentrations of unwanted components are present in the HTL oil produced. Less nutrients (N and P) can be recovered in HTL compared to wet extraction. HTL consumes more fossil energy and generates higher GHG emissions than wet extraction, while the production cost of fuel from HTL pathway is lower than wet extraction pathway. There is considerable uncertainty in the comparison of the energy consumption and economics of the HTL pathway and the wet extraction pathway due to different scenarios analysed in the assessment studies. To be able to appropriately compare methodologies, the conversion methods should be analysed from growth to upgradation of oil utilising sufficiently similar assumptions and scenarios. Based on the data in available literature, wet oil extraction is the more appropriate system for biofuel production than HTL. However, the potential of alternative extraction/conversion technologies, such as, non-destructive extraction, need to be further assessed.

AbstractAn arc‐furnace model consisting of non‐linear, time‐varying resistance, realized by the Alternative Transients Program (ATP), is presented in this paper. It is shown that the model is appropriate for flicker investigation in electrical networks supplying arc furnaces, constituting a valid alternative to the linear arc modelling realized by amplitude modulation of voltage generators. Block diagram of the procedure for ATP modelling of the nonlinear, time‐varying arc resistance, dynamic voltage‐current characteristic of the furnace electric arc, voltage and current arc waveforms and characteristic curves of an arc furnace, obtained by ATP simulation, are presented, referring to circuit parameters taken from an actual plant and to a sinusoidal law for arc‐length time variation. Aflickermeter ATP simulation is realized to derive equivalent voltage‐variation values in agreement with the actual flicker measurements.

Short term load forecasting plays an increasingly important role in Smart Grid. Short term load forecasting is also an important part of enterprise power system management. Providing accurate load time series data for a certain period of time in the future can enable enterprises to ensure the smooth operation of production while making a reasonable power plan, reducing power consumption and basic electricity charges, thus reducing the production cost of enterprises. In addition, lower electricity consumption means lower carbon dioxide emissions, which has far-reaching implications for sustainable development strategies. This paper presents a short-term load forecasting method based on time series. The model divides the time series data into four parts: trend item, period item, holiday item and error item. In the experiment part, this paper provides a set of preprocessing method flow. Aiming at the problem that the sampling rate of the current smart grid data is not constant, a data smoothing algorithm is proposed.

Abstract The central role of occupants for achieving energy savings in residential buildings is increasingly recognised. Simulation programmes able to take into account occupant behaviour are considered to be powerful tools for bridging the gap between the predicted and the actual energy consumption for new buildings. Nevertheless, the majority of residential buildings that will constitute the housing stock in 2050 have already been built today, therefore occupant behaviour and building simulation tools need to be fully exploited for supporting the renovation of existing housing stock. The aim of this paper is to explore the role of occupant behaviour modelling in supporting decision-makers dealing with the design of renovation strategies for residential buildings. An Italian multi-family public housing building is assumed as case study to estimate the influence of three dimensions linked with occupant behaviour – management of the thermostat, management of the heating system, variation of building characteristics – on energy heating consumption. The results show that, while the occupant behaviour influences the heating loads up to 1/3 in case of high level of building retrofit, the less the building is renovated, the higher is the behavioural impact in absolute terms of energy reduction. Therefore, in order to be effective, renovation strategies are required to design appropriate informative instruments at an early stage to support behaviour changes towards responsible energy consumption.