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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 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.

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.

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.

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.

Abstract Building energy performance is often inadequate given design goals. While different types of assessment methods exist, they either do not consider design goals and/or are not general enough to integrate new and innovative energy concepts. Furthermore, existing assessment methods focus mostly on the building and system level while ignoring more detailed data. With the availability and affordability of more detailed measured data, the increased number of measured data points requires a structure to organize these data. This paper presents the Energy Performance Comparison Methodology (EPCM), which enables the identification of performance problems based on a comparison of measured data and simulated data representing design goals. The EPCM is based on an interlinked building object hierarchy that structures the detailed performance data from a spatial and mechanical perspective. This research is developed and tested on multiple case studies that provide real-life context and more generality compared to single case studies.

Abstract Building energy performance is often inadequate given design goals. While different types of assessment methods exist, they either do not consider design goals and/or are not general enough to integrate new and innovative energy concepts. Furthermore, existing assessment methods focus mostly on the building and system level while ignoring more detailed data. With the availability and affordability of more detailed measured data, the increased number of measured data points requires a structure to organize these data. This paper presents the Energy Performance Comparison Methodology (EPCM), which enables the identification of performance problems based on a comparison of measured data and simulated data representing design goals. The EPCM is based on an interlinked building object hierarchy that structures the detailed performance data from a spatial and mechanical perspective. This research is developed and tested on multiple case studies that provide real-life context and more generality compared to single case studies.

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.