Electricity consumption dataset of a local energy cooperative

Real-world data collections are generally not easily available. Energy measurements from buildings, houses and other devices can be used within different areas of research while being employed to plan or train models, allowing the improvement of power grid energy efficiency or providing more insight on how an energy community can work. This paper provides a dataset concerning a Portuguese community of 172 households that are geographically close to each other, enabling the establishment of relationships among buildings and the analysis of a community's power consumption. In addition to the consumed energy values, the related local weather information is included in the data. The intersection of weather data and energy measurements can be helpful to train AI models, contributing to explain variations in energy consumption and the absolute values of the energy readings. The inclusion of these weather parameters aims to unveil features that can correlate to the energy measurements, enabling them to be used in multiple areas of research. Hence, it will provide added value to the data as it can be reused to explore Machine Learning algorithms or community energy planning by grid operators.

A comprehensive study to estimate income and price elasticities of household electricity consumption using Auto-metrics

This study focuses on understanding family electricity consumption behaviors in response to income and price changes from 1994 to 2022 across 12 prominent European countries. We employ a unique econometric approach, Auto-selection Models, to analyze the nuances of energy demand elasticity. Our methodology includes the use of saturation techniques, which are highly effective in identifying anomalies and discontinuities in the data, ensuring the reliability of our results. The Auto-metrics method streamlines the model selection process and enhances the accuracy of elasticity predictions. We use Error Correction Models (ECMs) for each country to examine the long-term equilibrium relationships among key variables such as energy consumption, household income, electricity prices, and weather patterns, taking into account any observed anomalies and significant structural changes. The findings reveal varying levels of income and price elasticity across the countries, reflecting their unique economic and climatic conditions. The study's results hold significant implications for policymaking. By recognizing and adapting to the varied characteristics of electricity demand elasticity, energy policies can be more accurately tailored at both the national and European Union levels.

Electricity consumption of anesthesia workstations and potential emission savings by avoiding standby

BACKGROUND

Anesthesiology has a relevant carbon footprint, mainly due to volatile anesthetics (scope 1 emissions). Additionally, energy used in the operating theater (scope 2 emissions) contributes to anesthesia-related greenhouse gas (GHG) emissions.

OBJECTIVES

Optimizing the electricity use of medical devices might reduce both GHG emissions and costs might hold potential to reduce anaesthesia-related GHG-emissions and costs. We analyzed the electricity consumption of six different anesthesia workstations, calculated their GHG emissions and electricity costs and investigated the potential to reduce emissions and cost by using the devices in a more efficient way.

METHODS

Power consumption (active power in watt , W) was measured with the devices off, in standby mode, or fully on with the measuring instrument SecuLife ST. Devices studied were: Dräger Primus, Löwenstein Medical LeonPlus, Getinge Flow C, Getinge Flow E, GE Carestation 750 and GE Aisys. Calculations of GHG emissions were made with different emission factors, ranging from very low (0.09 kg CO2-equivalent/kWh) to very high (0.660 kg CO2-equivalent/kWh). Calculations of electricity cost were made assuming a price of 0.25 € per kWh.

RESULTS

Power consumption during operation varied from 58 W (GE CareStation 750) to 136 W (Dräger Primus). In standby, the devices consumed between 88% and 93% of the electricity needed during use. The annual electricity consumption to run 96 devices in a large clinical department ranges between 45 and 105 Megawatt-hours (MWh) when the devices are left in standby during off hours. If 80% of the devices are switched off during off hours, between 20 and 46 MWh can be saved per year in a single institution. At the average emission factor of our hospital, this electricity saving corresponds to a reduction of GHG emissions between 8.5 and 19.8 tons CO2-equivalent. At the assumed prices, a cost reduction between 5000 € and 11,600 € could be achieved by this intervention.

CONCLUSION

The power consumption varies considerably between the different types of anesthesia workstations. All devices exhibit a high electricity consumption in standby mode. Avoiding standby mode during off hours can save energy and thus GHG emissions and cost. The reductions in GHG emissions and electricity cost that can be achieved with this intervention in a large anesthesiology department are modest. Compared with GHG emissions generated by volatile anesthetics, particularly desflurane, optimization of electricity consumption of anesthesia workstations holds a much smaller potential to reduce the carbon footprint of anesthesia; however, as switching off anesthesia workstations overnight is relatively effortless, this behavioral change should be encouraged from both an ecological and economical point of view.

The link between electricity consumption and stock market during the pandemic in Türkiye: a novel high-frequency approach

This article examines the relationship between electricity consumption and the stock market in the Turkish economy during the COVID-19 pandemic. A novel high-frequency model is used, incorporating the hourly energy consumption and Borsa Istanbul (BIST) National stock market index variables. To determine the effect of electricity consumption on the stock market index and vice versa, a high-frequency VAR-based spillover approach, time-varying Granger causality, and time-varying Bayesian VAR analysis are employed. The findings reveal a positive and weak relationship between electricity consumption and the stock market but it has a time-varying nature in an emerging market context in the post-COVID-19 period in the Turkish economy.

Investigating the role of smart technologies, financial, and environmental innovations in tackling the ecological sustainability: a global pathway toward low carbon energy transition

Since the beginning of the twenty-first century, the rapid development of modern technologies has brought unprecedented social prosperity to mankind as technologies penetrate every sector of the economy. These technologies have given a new dimension to the energy sector. The key purpose of this study is to investigate the crucial impact of technological revolutions, namely, smart grids, smart devices, financial innovations, and environmental innovations, on greenhouse gas emissions (GHGs). To this end, the study utilized data from European, Asian, Middle Eastern, and African countries and employed first- and second-generation methods, such as DOLS, FMOLS, and CS-ARDL models. The research shows that smart grids are the only factor in reducing GHGs, regardless of geographic division. Hence, linking smart grid resources to climate change goals requires short-term deployment strategies with a clear long-term vision and the fundamental goal of transforming the power structure into a net zero-emission system. The study also demonstrates that the emergence of ICT in electricity consumption has not yet reached a level that can promote environmental excellence. The study documented the critical role of financial innovation and environmental innovation in addressing environmental degradation.

An EKC-based modelling of CO2 emissions, economic growth, electricity consumption and trade openness in Serbia

Understanding the complex interactions between the economy and the environment is crucial for promoting sustainable development and mitigating the negative impact of human activities on the Planet. The importance of this issue for Serbia is evident as the country strives to balance economic growth and environmental protection to ensure a sustainable and resilient future. Therefore, the main objective of this study is to investigate and model the relationship between CO2 emissions, economic growth, electricity consumption, and trade openness in Serbia. Initially, an Autoregressive Distributed Lag (ARDL) model is used to characterize the Environmental Kuznets Curve (EKC) using data from the period from 1995 to 2019, followed by the construction of a bootstrap logistic regression model to predict environmental quality in Serbia. Long-term estimates of the model confirm an inverted U-shaped relationship, where all three variables exert a statistically significant influence on CO2 emissions. In the short run, however, a causal relationship is only observed between electricity consumption and CO2 emissions. The logistic regression results show that all three factors significantly influence environmental quality. The study proposes policy recommendations for Serbia, such as promoting sustainable economic growth, implementing long-term programs to reduce CO2 emissions, reviewing trade policies to prioritize sustainable practices, and investing in renewable energy sources to reduce emissions.

Sustainable electricity consumption in South Africa: the impacts of tourism and economic growth

The current study examines sustainable electricity consumption for economic growth in a small open and tourist economy. The energy-tourism nexus is evaluated for the relationship between sustainable electricity consumption and the international tourist arrival for the South African economy. The present study leverages on annual frequency data for South Africa from 1995 to 2019 for empirical analysis using the ARDL technique. Accordingly, empirical findings indicate a significant direct connection between the sustainable electricity consumption and the international tourism arrival; the study affirms that tourism-induced energy hypothesis is valid in South Africa. However, from a policy standpoint, alternative energy efficiency mechanisms such as renewable energy systems and emancipation of current energy management capabilities are recommended in South Africa. This is necessary for sustainable eco-friendly tourism that engenders clean energy consumption for the study area. More insights into policy caveats are presented in the concluding section.

Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda

The COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid model with better prediction accuracy using COVID19 data. Existing datasets are reviewed, and their limited generalization potential for the COVID19 period is highlighted. A dataset of 96 residential customers, comprising 36 and six months before and after the pandemic, is collected, posing significant challenges for current models. The proposed model employs convolutional layers for feature extraction, gated recurrent nets for temporal feature learning, and a self-attention module for feature selection, leading to better generalization for predicting EC patterns. Our proposed model outperforms existing models, as demonstrated by a detailed ablation study using our dataset. For instance, it achieves an average reduction of 0.56% & 3.46% in MSE, 1.5% & 5.07% in RMSE, and 11.81% & 13.19% in MAPE over the pre- and post-pandemic data, respectively. However, further research is required to address the varied nature of the data. These findings have significant implications for improving ELF algorithms during pandemics and other significant events that disrupt historical data patterns.

Water consumption of electric power system in China: from electricity generation to consumption

Understanding the water requirement of electricity generation is critical to the development of both electricity and water systems, while the water consumption of the whole electric power system remains unrevealed. Here, we examine the water consumption driven by electricity generation, transmission, and consumption in China, finding that 14 billion m3 of freshwater is consumed by electricity generation in 2019 and that 2.5 billion m3 of freshwater was virtually transferred via electricity transmission. Nationally, the freshwater consumption per unit of electricity generation was 1.9 m3/MWh. Based on the state-of-the-art electricity transmission data, we find that 59% of the transported electric power was from water-scarce provinces and 0.7 billion m3 of freshwater was lost due to the electricity loss in transmission lines in China. It is essential to link water resources with the whole electric power system (production, transmission, and consumption) rather than only part of the power system as in previous research.

COVID-19 triggered residential behavioral changes and electricity consumption of detached houses in Japan

Many studies conducted previously have reported that due to lockdowns or stay-at-home orders associated with the COVID-19 pandemic in April 2020 residential power consumption has increased in countries, particularly in cities worldwide. This study compared the power consumption of 1,339 detached houses in Japan over the past three years as well as a year after the pandemic and analyzed living behavioral changes in the 12 months after the pandemic using a questionnaire survey of occupants. As of March 2021, which is after 12 months of the beginning of the pandemic, it was confirmed that the way of life had returned to almost normal, and as a factor in increasing consumption, working from home would remain the only behavioral change that may take root in Japanese society.

Is temperature change a key driver of the regional differences in electricity consumption of the economic sectors in Spain (2000-2016)?

Climate change has caused significant changes in temperature with different consequences depending on the geographical location of the regions, affecting among other aspects, electricity consumption (EC). Spain being a country that encompasses so many different temperature zones, this work analyses EC per capita among the Autonomous Communities (AC) of Spain through a spatial-temporal decomposition analysis during the 2000-2016 period. The regional differences are explained by four decomposition factors: intensity, temperature, structural and per capita income. The temporal decomposition results show that temperature changes in Spain between 2000 and 2016 have substantially affected the per capita EC. Likewise, it has been noted that in the 2000-2008 period, the temperature effect mainly acted as an inhibitor compared to the 2008-2016 period, in which an increase in the days of extreme temperature acted as a driver. The spatial decomposition reveals that the structural and energy intensity effects contribute to the AC moving away from average figures, while the temperature and income effects contributes to reducing the differences depending on the location of the AC. The results enable to determine the importance of establishing public policy measures aimed at improving energy efficiency.

Identifying the electricity-saving driving behaviors of electric bus based on trip-level electricity consumption: a machine learning approach

Electric buses (EBs) are gaining popularity worldwide as a more sustainable and eco-friendly alternative to diesel buses (DBs). Electricity-saving driving plays a crucial role in minimizing an EB's energy consumption, subsequently leading to an extended driving range. This study proposes a machine learning-based framework for identifying electricity-saving EB driving behaviors during various driving stages, including running on road segments, entering bus stops/intersections, and exiting bus stops/intersections. The proposed random forest (RF) model effectively evaluates the energy consumption level using EB drivers' historical driving data under different scenarios. Specifically, the electricity consumption factor (ECF), as the evaluation index, is divided into three categories to determine the implicit relationship between driving behavior and energy consumption. The results indicate that the classification accuracy of RF models surpasses 90%, which highlights the effectiveness in accurately identifying energy-efficient EB driving behaviors. In addition, the Shapley additive explanations (SHAP) and partial dependency plots (PDPs) are utilized to visualize and interpret the results of RF models. A speed interval of 30-40 km/h is identified as the most energy-efficient range for EB running on a road segment. Findings from this study can be applied to targeted optimization of electricity-saving driving strategies in different driving scenarios to improve the overall efficiency and sustainability of the transportation system.

How technological innovation and electricity consumption affect environmental quality? A road map towards achieving environmental sustainability

Still the world witnessed an upturn in environmental degradation in spite of commitment to climate change across the nations. However, this study attempts to examine linkages among environmental degradation, technological innovation, and electricity consumption in India from 1981 to 2018 using time series data. To examine the long-run equilibrium relationship among the studied variables, we used robust econometric methods such as the autoregressive distributed lag (ARDL), fully modified ordinary least square (FMOLS), and dynamic ordinary least square (DOLS) methods. Furthermore, Granger causality also investigates through the vector error correction model (VECM) model, to assess inter-connotation among the underlying variables. From our empirical findings, urbanization, financial development, and technological innovation have a negative impact on carbon emissions, indicating long-term improvements in environmental quality. While economic development and electricity consumption are deteriorating environmental quality in India. The study's findings suggest that policymakers should prioritize renewable energy, which decreases environmental damage without impeding economic growth.

Sectoral analysis of electricity consumption during the COVID-19 pandemic: Evidence for unregulated and regulated markets in Colombia

We conduct a sectoral analysis of electricity consumption during the Coronavirus disease 2019 (COVID-19) pandemic for the primary sectors that make up Colombia's unregulated and regulated markets. Applying a model of seemingly unrelated regression equations to examine data between February 2015 and May 2021, we evidence the recomposition of electricity consumption related to mandatory preventive isolation during the pandemic. Average consumption in the residential sector increased by 16.9% as working from home became prevalent. In contrast, unregulated market sectors subjected to quarantines presented a significant decrease in consumption, up to 32% in the financial sector. While industries that were not subjected to mandatory confinement, such as health, food (agriculture), and water supply, had no significant effect. Our results are relevant for informing demand forecasts and planning network expansions to guarantee the reliability of the supply as pandemic practices such as working from home become permanent.

Spatially explicit carbon emissions by remote sensing and social sensing

Scientific simulation of carbon emissions is an important prerequisite for achieving low-carbon green development and carbon peak and carbon neutralization. This study proposed a carbon emissions spatialization method based on nighttime light (NTL) remote sensing and municipal electricity social sensing. First, the economics-energy comprehensive index (EECI) was proposed by integrating the NTL and municipal electricity consumption (EC) data. Second, the carbon emissions were spatialized at a fine scale based on NTL, EC, and EECI, respectively. Finally, the geographical detector model was applied to quantify the influencing factors on carbon emissions from the perspectives of individuals and interactions. Results show that combining remote sensing and social sensing data helps depict carbon emissions accurately. The factor analysis found that GDP and population were the basis of carbon emissions, while the secondary industry and urbanization rate were the direct factors. This study is expected to provide constructive suggestions and methods for emission reduction, carbon peak, and carbon neutrality in high-density cities in China.

Economic growth and global warming effects on electricity consumption in Spain: a sectoral study

This paper analyzes the effect of certain factors on electricity consumption in Spain at a sectoral level. An electricity consumption function has been estimated by using panel data, depending on gross value added (GVA), temperatures, capitalization, and human capital. This function is obtained for total productive electricity consumption and for the agricultural, construction, industrial, service, and public administration sectors, referring to the 17 Autonomous Communities of Spain for the 2000-2013 period. The obtained results show important sectoral differences in the effect that GVA has on electricity consumption, indicating a positive and increasing effect of temperatures above 22 °C in the total economy and in the tertiary sector, and a negative effect of temperatures below 18 °C in some sectors. These results may indicate that global warming may induce an electricity demand growth in Spain, especially related to cooling needs. The results also highlight the positive effects of capitalization in all sectors, and the negative effects of human capital, except for the public administration sector. In this context, it may be appropriate to carry out policies that mitigate this consumption growth, reinforcing energy efficiency measures, and human capital investments.

Role of financial development, foreign direct investment inflow, innovation in environmental degradation in Pakistan with dynamic ARDL simulation model

The purpose of this research is to determine the impact of innovation, economic growth, financial development, trade, foreign direct investment (FDI), electricity consumption, and urbanization on the environmental degradations in Pakistan. This study has employed the dynamic autoregressive distributed lag model (ARDL), to investigate the actual change in the independent variables and its impact on the dependent variable through graphs. The findings demonstrate that energy consumption, GDP growth, urbanization, and trade negatively influence the carbon emissions in the short term. On the other hand, the findings indicate that in the long term, only GDP growth and trade had a significantly negative impact on emissions. Urbanization has a positive and considerable impact on the emissions of carbon dioxide in the long run. On the other hand, financial development and foreign direct investment (FDI) help reduce the environmental degradation in the short term and long term. Moreover, innovation positively affects the carbon emissions in both the long and short run. Policy recommendations are given based on the findings of this study.

The transaction behavior of cryptocurrency and electricity consumption

Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production, increasing network hashrates and electricity consumption. Growth in network hashrates has further crowded out small cryptocurrency investors owing to the heightened costs of mining hardware and electricity. These changes prompt cryptocurrency miners to become new investors, leading to cryptocurrency price increases. The potential bidirectional relationship between cryptocurrency price and electricity consumption remains unidentified. Hence, this research thus utilizes July 31 2015-July 12 2019 data from 13 cryptocurrencies to investigate the short- and long-run causal effects between cryptocurrency transaction and electricity consumption. Particularly, we consider structural breaks induced by external shocks through stationary analysis and comovement relationships. Over the examined time period, we found that the series of cryptocurrency transaction and electricity consumption gradually returns to mean convergence after undergoing daily shocks, with prices trending together with hashrates. Transaction fluctuations exert both a temporary effect and permanent influence on electricity consumption. Therefore, owing to the computational power deployed to wherever high profit is found, transactions are vital determinants of electricity consumption.

A multivariate causality analysis of CO2 emission, electricity consumption, and economic growth: Evidence from Western and Central Africa

The vector error correction model is used to examine the short- and long-run impacts of electricity consumption and economic growth on CO₂ emissions in Western and Central Africa from 1970 to 2020. This paper adopted time series vector error correction model (VECM) approach to conduct stationarity test, cointegration test, stability test, and Granger causality test. Cointegration tests are used to examine the long-run impact of electricity consumption and economic growth on CO₂ emissions. It was revealed that CO₂ emission, electricity consumption and economic growth are co-integrated. Electricity consumption and economic growth have a significant and positive effect on CO₂ emission. The study also revealed that the adjustment process is not driven by electricity consumption, and anytime there is a deviation from the long-run equilibrium, economic growth and CO₂ emission adjust to restore the long-run equilibrium. From the short-run Granger causality, electricity consumption and economic growth do not Granger cause CO₂ emissions. However, past values of CO₂ emissions have an effect on the present value of economic growth. Generally, long-run dynamics of electricity consumption and economic growth were established to have a greater impact on CO₂ emission than the short-run dynamics. Hence, it is important to promote green economic concepts in the area.

Does awareness of environmental pollution increase electricity consumption? A view from household survey of China

Household electricity conservation is a non-negligible aspect of environmental pollution with growing importance for an eco-friendly economy and society. However, debates regarding household electricity consumption have placed more emphasis on the physical attributes of the dwellings, demographic characteristics and the socioeconomic behaviour of households; few studies have directly discussed the awareness of environmental pollution. Based on the 'China Family Panel Studies' surveys with an extracted 8249 households in 25 provinces from 2014 to 2018, we analyse whether or not and to what extent the awareness of environmental pollution impacts household electricity consumption. The study finds that the awareness of environmental pollution rather than actual environmental pollution increases household electricity consumption. The finding is robust under various model specifications. Given a 1% increase in the awareness of environmental pollution, households living in southern China, located in eastern China or living in an urban area were found to have higher electricity consumption. To address possible estimation bias due to self-selection, we design a quasi-policy-shock variable to describe the severity of the perceived environmental pollution and run the propensity score matching regression (PSM). The finding still holds.

An empirical assessment of electricity consumption and environmental degradation in the presence of economic complexities

To a large extent, the theories and concepts behind the effect of ecological footprint have been the paramount concern of the recent literature. Since the rising and falling of environmental degradation have been a continuous issue since the first phase of development, determinants such as economic complexity may play a critical role in achieving long-term sustainable development in the framework of environmental Kuznets curve (EKC) paradigm. Therefore, this research expands on the notion of an EKC paradigm for the world's top ten most complex economies by considering four variables, such as real GDP per capita, electricity consumption, trade openness, and a new putative factor of environmental obstacle, the economic complexity index (ECI). This is one of the first studies to look at the impact of ECI on the ecological footprint of a specific sample from 1998 to 2017. The findings demonstrate a continuous inverted U-shaped link between real GDP per capita, the square of real GDP per capita, and ecological footprint. The EKC hypothesis is found to be valid in the long term in the examined complex economies. The findings of the panel autoregressive distributed lag (ARDL) of the pooled mean group (PMG) and fully modified ordinary least squares (FMOLS) estimations demonstrate that in the long term, electric power usage contributed to the carbon footprints. Furthermore, the economic complexity index and trade openness increase environmental performance over time. To determine if there is causation between the variables, we employ the panel vector error correction model (VECM) framework. Particularly, the results show unidirectional causality running from electric power consumption to ecological footprint and bidirectional causal relationship between (1) economic growth and ecological footprint; (2) square of economic growth and ecological footprint; (3) economic complexity index and ecological footprint; and (4) trade openness and ecological footprint.

Decomposition of residential electricity-related CO2 emissions in China, a spatial-temporal study

The COVID-19 pandemic brings a surge in household electricity consumption, thereby enabling extensive research interest on residential carbon emissions as one of the hot topics in carbon reduction. However, research on spatial-temporal driving forces for the increase of residential CO₂ emissions between regions still remains unknown in terms of emissions mitigation in post-pandemic era. Therefore, we studied the residential CO₂ emissions from the electricity consumption of China during the period 1997-2019. Afterward, the regional specified production emission factors, combining with electricity use pattern, living standard and household size, were modelled to reveal the spatial-temporal driving forces at national and provincial scales. We observed that the national residential electricity-related CO₂ increased from 1997 to 2013, before fluctuating to a peak in 2019. Guangdong, Shandong and Jiangsu, from East China were the top emitters with 27% of the national scale. The decomposition results showed that the income improvement was the primary driving force behind the emission increase in most provinces, while the household size and production emission effects were the main negative effects. For the spatial decomposition, differences in the total households between regions further widen the gaps of total emissions. At the provincial scale of temporal decomposition, eastern developed regions exhibited the most significant decrease in production emissions. In contrast, electricity intensity effect showed negative emission influences in the east and central regions, and positive in north-eastern and western China. The research identified the different incremental patterns of residential electricity-related CO₂ emissions in various Chinese provinces, thereby providing scientific ways to save energy and reduce emissions.

The discrepancies in the impacts of COVID-19 lockdowns on electricity consumption in China: Is the short-term pain worth it?

The COVID-19 pandemic caused severe economic contraction and paralyzed industrial activity. Despite a growing body of literature on the impacts of COVID-19 mitigation measures, scant evidence currently exists on the impacts of lockdowns on the economic and industrial activities of developing countries. Our study provides an empirical assessment of lockdown measures using 298,354 data points on daily electricity consumption in 396 sub-industries. To infer causal relationships, we employ difference-in-differences models that compare cities with and without lockdown policies and provide quantitative evidence on whether the long-term gain of lockdowns outweighs the short-term loss. The results show that lockdown policies led to a significant short-term drop in electricity consumption of 15.2% relative to the control group. However, the electricity loss under the no-lockdown scenario is 2.6 times larger than that under the strict lockdown scenario within 4 months of the outbreak. Discrepancies in the impacts among industries are identified, and even within the same industry, lockdowns have heterogeneous effects. The impact of lockdowns on small and medium-sized enterprises in developing countries is seriously underestimated, raising concerns about the distributional impact of subsidy measures. This study serves as a crucial reference for the government when facing public health emergencies and shocks to support better policies.

The drinking water disinfection performances and mechanisms of UVA-LEDs promoted by electrolysis

In this study, the UVA (Ultraviolet A) drinking water disinfection was promoted by electrolysis. The influences of the UVA, electrolysis current, bubbling and temperature were investigated. The disinfection mechanisms and bacterial reactivation had been studied. The results revealed that the treatment time needed to reach the DL (detection limit, about 5.4 log removal) was shortened from 180 to 80 min by the electrolysis. The total electricity consumption decreased from about 126-57.0 kJ/L. Compared with increasing the UVA irradiation, increasing the electrolysis current in a certain range was more preferred to improve the disinfection rate. Oxygen bubbling or higher temperature could enhance the E. coli inactivation. The quenching experiment and EPR (Electron paramagnetic resonance) detection confirmed that ROSs (¹O₂, ·O₂^- and ·OH) played important roles for the disinfection. Compared with the treatment with UVA alone, the cell membrane damage was more severe by the promoting method. In addition to the dramatically reduced enzyme activity, the synergistic process degraded most of the bacterial genomic DNA, and the bacteria were completely killed. Therefore, hybrid with electrolysis is a better way for the application of the UVA-LED disinfection.

Globalization toward environmental sustainability and electricity consumption to environmental degradation: does EKC inverted U-shaped hypothesis exist between squared economic growth and CO2 emissions in top globalized economies

The study inspects the inverted U shape of the environmental Kuznets curve (EKC) hypothesis following the influence of economic growth on CO₂ emissions and the reaction of electricity consumption and globalization toward CO₂ emissions in top globalized economies. This study has taken the data of the top 9 globalized countries from 1990 to 2019 while adopting fully modified ordinary least squares and dynamic ordinary least squares panel cointegration approaches to determine the long run effects and Dumitrescu and Hurlin panel causality for the directions of the causality among the variables. According to the long-term findings of the research, economic growth and electricity consumption substantially contribute to CO₂ secretions. On the other hand, the squared growth and globalization mitigate CO₂ emissions and contribute to environmental sustainability. However, the inverse influence of squared growth on CO₂ emissions shows the presence of the inverted U shape of the EKC hypothesis. Furthermore, Dumitrescu and Hurlin causality measures have shown the bi-directional causality of electricity consumption and economic growth with CO₂ emissions and globalization with economic growth. At the same time, unidirectional causality exists from globalization to CO₂ emissions, economic growth to electricity consumption, and electricity consumption to globalization. The study recommends long-term globalization and sustainable development projects to ensure environmental sustainability in these globalized economies.

A near real-time economic activity tracker for the Brazilian economy during the COVID-19 pandemic

During the COVID-19 pandemic, policymakers needed to assess the impact of large monetary and fiscal policy interventions in as close to real time as possible-yet existing survey-based indicators are usually released monthly or quarterly. The use of high-frequency data to track economic activity has become widespread. This paper constructs a near real-time economic activity indicator for the Brazilian economy during the COVID-19 pandemic. Brazil's integrated national electricity sector, which covers over 98% of the population, allows us to construct an economic activity indicator based solely on electricity consumption data that are available at near real time and accounts for activity in the large informal sector of the economy. We construct our indicator by isolating the variability in electricity consumption that is not related to economic activity, then measure how well monthly and quarterly versions of our indicator track against standard economic indicators. The results show strong correlation with standard indicators, notably during economic shocks.

Spatial spillover effects and action paths of electricity consumption driven by China's financial development based on global co-integration

Although studies on the influencing factors of electricity consumption are rich, the focus on the relationship between financial development and electricity consumption is scarce due to the characteristics of financial sector. In fact, the financial development cannot only increase electricity consumption, but also have the spatial spillover effects. Based on the global spatial modeling techniques, the long-term and short-term relationship between financial development and electricity consumption is examined, and the intermediary effect of financial development on electricity consumption through economic growth, urbanization, and industrial structure optimization is also verified. Results show that there is a global co-integration relationship between financial development, economic growth, urbanization, industrial structure optimization, and China's electricity consumption, rather than a local co-integration relationship. When the short-term change of electricity consumption deviates from the equilibrium state, the global error correction mechanism can promote the unbalanced system to return to equilibrium from time and spatial dimension. This study confirms not only the spatial spillover effects, but also heterogeneous influences of financial development on electricity consumption, which provides new evidence to make relevant policies.

Linking electricity demand and economic growth in China: evidence from wavelet analysis

The study empirically examines the association between electricity demand and economic growth in China in a time-frequency framework. Wavelet coherence analysis and phase difference methods are applied to find the co-movement and causality between variables using monthly data for 1999 to 2017 time period. The results of the wavelet power spectrum show that both series have high fluctuations at high frequencies. The findings of wavelet coherence reveal co-movements between electricity demand and economic growth at different frequency levels. However, this association is stronger at low-frequency levels. Evidence from the phase difference indicates that electricity is causing economic growth with a positive sign. The results of wavelet-based correlation also show a high correlation between these two variables. For robustness analysis, linear and nonlinear causality tests are applied to find causality between variables over time. Both linear and nonlinear causality tests reveal bidirectional causality between variables. It corroborates the result of wavelet causality that both variables cause each other at different frequency levels.

The impact of online education on carbon emissions in the context of the COVID-19 pandemic - Taking Chinese universities as examples

While the COVID-19 pandemic has had various impacts on economic and social development, it may have partially reduced human energy use, thereby helping achieve the goals of reducing carbon emissions and promoting carbon neutrality. During the pandemic, online education was widely used to replace traditional education all over the world. There is a lack of empirical studies on whether and to what extent the change of education model can reduce carbon emissions. Taking Chinese universities as cases, this study, concentrating on two main elements - transportation and electricity consumption - constructs a model and calculates the impact of online education on carbon emissions. The results show that online education can significantly reduce energy consumption and lower carbon emissions. In the field of higher education alone, the carbon emissions reduction caused by online education in half a year is equivalent to the total carbon emissions reduction of college students caused by online education during the half-year is equivalent to the total carbon emissions in 1.296 h in China, 2.688 h in the United States, 5.544 h in India, 12 h in Japan and 3.864 h in European countries of OECD. Therefore, this study suggests that the impact of online education on carbon emissions should be further studied, online education should be promoted through legislation and other systemic measures, and the goals of carbon emissions and carbon neutrality should be explored further within the field of education.

Nexus among electricity consumption, foreign direct investment and aggregate economic activity towards Nigeria's economic performance: evidence from a trivariate causality model

It is evident that there is nexus among electricity consumption, foreign direct investment and aggregate economic activity. Unfortunately, the causal relationship among the three variables in Nigeria based on modern econometric methods, recent time-series data and ways that sufficiently cater for inflation and population growth has not been adequately investigated. This study, among other things, used a trivariate vector error correction model, autoregressive distributed lag bounds test for cointegration and Granger causality test to analyse the causal relationship among electricity consumption, foreign direct investment and aggregate economic activity based on time-series data from 1970 to 2018. The study found the presence of neutral causality between electricity consumption and aggregate economic activity in the short run as well as unidirectional causality from aggregate economic activity to electricity consumption in the long run. The study also found the presence of unidirectional causality from foreign direct investment to electricity consumption as well as neutral causality between foreign direct investment and aggregate economic activity in both the short run and the long run. It is therefore recommended that steps should be taken to adequately increase foreign direct investment and aggregate economic activity in ways that will guarantee an optimal increase in electricity consumption in Nigeria.

The impact of foreign direct investment, tourism, electricity consumption, and economic development on CO2 emissions in Bangladesh

The study's goal is to investigate the impact of foreign direct investment (FDI), tourism, electricity consumption, and economic development on CO₂ emissions in Bangladesh between 1990 and 2019. Empirical results reveal that FDI, electricity consumption, and economic development variables have significant and positive long-term effects on CO₂ emissions. Tourism, on the other hand, has a long-term negative effect. The square of the GDP variable has a substantial negative coefficient. This indicates that in Bangladesh, the nexus between CO₂ emissions and economic development is U-shaped inverted. As a result, the EKC postulate is proven to be correct. In the short term, electricity consumption, economic development, GDP², and tourism have no substantial effect on CO₂ emissions. Only the coefficients of FDI are negative and significant. The expected ECM coefficients are also negative and statistically significant. According to these data, the system as a whole adjusts at a rate of 60%. The Granger causality study reveals one direction of causation between electricity consumption and CO₂ emissions, CO₂ emissions and economic development, electricity consumption and economic development, FDI, and CO₂ emissions.

Cost of raising discharge standards: A plant-by-plant assessment from wastewater sector in China

China has implemented increasingly stringent effluent standards for wastewater treatment plants (WWTPs) to protect the aquatic environment, but at the cost of more resource consumption and greenhouse gas emissions. To elaborate tradeoffs between the elevated standard and the additional burden, we compile a 10-year inventory of 6032 WWTPs across China to estimate the impacts of changes in effluent pollutant concentration on operating costs and electricity consumption. Coupled with the increasing demand for wastewater treatment, upgrading standards to the Special Discharge Limit (SDL) by 2030 would increase electricity consumption and operating costs of the wastewater treatment sector by 86.59% and 70.44% compared to the status quo in 2015. The electricity consumption-induced GHG emissions would also increase by 72.21%, which accounts for 29.16% of total emissions in the domestic wastewater treatment sector. Substantial regional differences exist in terms of upgrade-induced resource burden. Less developed regions generally suffer more stress when encountering similar standards elevation. With large-scale microdata, our findings deepen the understanding of the potential cost of raising standards and provide insights into region-customized pollutant effluent standards implementation.

Air pollution and post-COVID-19 work resumption: evidence from China

To cope with the coronavirus disease (COVID-19), national or sub-national regions have carried out many powerful anti-pandemic measures such as locking down, which may improve their regional air quality. This paper examines the relation between regional air pollution and work resumption from a novel post-pandemic perspective. Using a unique panel dataset on China's detailed industrial electricity consumption, this paper does not find a positive relation between post-COVID-19 work resumption and regional air pollution during China's early-stage recovery. This result is obtained after controlling for province and date fixed effects, as well as local weather conditions. However, the positive relations are found in a particular sub-sample of large industrial enterprises and a particular sub-sample of April. These findings indicate that large industrial enterprises may recover first, and the resumption is progressing gradually. Finally, several policy implications are provided, which are essentially helpful for other countries' post-pandemic recovery.

A novel grey power-Markov model for the prediction of China's electricity consumption

Forecasting the electricity consumption has always played an important role in the management of power system management, which requires higher forecasting technology. Therefore, based on the principle of "new information priority", combined with rolling mechanism and Markov theory, a novel grey power-Markov prediction model with time-varying parameters (RGPMM(λ,1,1)) is designed, which overcomes the inherent defects of fixed structure and poor adaptability to the changes of original data. In addition, in order to prove the validity and applicability of the prediction model, we have used the model to predict China's total electricity consumption, and have compared it with the prediction results by a series of benchmark models. The result shows that the can better adapt to the characteristics of electricity consumption data, and it also shows the advantages of the proposed forecasting model. In this paper, the proposed forecasting model is used to predict China's total electricity consumption in the next six years from 2018 to 2023, so as to provide certain reference value for power system management and distribution.

The role of electricity consumption, capital, labor force, carbon emissions on economic growth: implication for environmental sustainability targets in Nigeria

Despite consistent investments, grants, and other concessions in the power sector, nationwide power outages still remain an issue, even in 2020, disrupting business operations, contributing to huge recurrent expenses on generators and alternative sources of electricity in homes, businesses, and institutions. In this paper, we examine the role of electricity consumption on economic growth, while controlling for labor, capital, and carbon emissions, using the autoregressive distributed lag (ARDL) and the novel dynamic ARDL (DYNARDL) simulation analysis over the periods 1981-2019. Empirical results show that electricity consumption, capital, and labor exert positive inelastic impacts, while carbon emissions exert negative inelastic significant impact on economic growth within the period under investigation. From policy standpoint, we are of the opinion that stable supply and consumption of electricity can possibly boost economic growth and engender social stability in Nigeria. Thus, there is a need to strengthen the effectiveness of power sector and its energy generating agencies by ensuring periodic replacement of worn-out equipment in terms of adequately financed and efficient labor in order to enhance the contribution of the sector on economic growth, while in terms of environmental degradation, policy makers should work towards promotion of green economy for a sustainable economic growth and environment in Nigeria.

The real economic costs of COVID-19: Insights from electricity consumption data in Hunan Province, China

The COVID-19 pandemic has caused extreme economic fluctuations. However, the magnitude of the economic cost of this extreme event remains challenging to quantify. The impact of the COVID-19 pandemic on the economy is estimated through firm-level electricity consumption data from Hunan province, China. Specifically, a difference-in-differences (DID) model was employed to estimate the real economic costs. The results indicate that electricity consumption in Hunan Province dropped by 27.8% during the early stage of the COVID-19 pandemic. Manufacturing and the transportation industry suffered the most severe declines. Electricity consumption began to recover after the virus was controlled. We suggest that government departments should take full measures to prevent and control COVID-19 outbreaks and associated economic impacts, in conjunction with preparing for economic recovery, deploying targeted measures to support different industries in response to the heterogeneity COVID-19 pandemic impacts. The COVID-19 has changed people's living habits and brought a new direction, the Internet industry, of economic growth. Hunan Province needs to accelerate the digital empowerment of traditional industries, develop the Internet, 5G technology, and new digital infrastructure to offset the negative impact of the COVID-19 pandemic. Electricity consumption is an applicable index in estimate the real economic cost of extreme events.

Do higher education research and development expenditures affect environmental sustainability? New evidence from Chinese provinces

Even though higher education R&D expenditures (HEEXP) are important determinants of economic growth that facilitate science, technology, new ideas, and innovation, yet its effect on environmental sustainability remains unexplored. This paper examines the nexus between HEEXP and carbon dioxide emissions (CO₂e), followed by control variables such as electricity consumption (EC), foreign direct investment (FDI), gross domestic product (GDP), and total population (TP) for the period 2000Q1-2019Q4. Data were evaluated using different tests, e.g., the cross-sectional dependence test, cross-sectionally augmented Dickey-Fuller unit root test, Westerlund error-correction-based panel cointegration test, mean group, augmented mean group, common correlated effects mean group, and Dumitrescu-Hurlin panel causality test. First, the results validated the cointegration association among HEEXP, EC, FDI, GDP, TP, and CO₂e. Second, the finding showed significant long-term negative nexus between HEEXP and CO₂e. Third, the findings indicated that electricity consumption, foreign direct investment, gross domestic product, and total population are the important factors that intensify the overall situation of CO₂e. Fourth, the results indicated that there exists bidirectional causality between EC and CO₂e; FDI and CO₂e; GDP and CO₂e; POP and CO₂e; and HEEXP and CO₂e. This paper's findings call for devising policies and strengthening financial support to induce higher education for developing green patents.

Carbon neutrality target for leading exporting countries: On the role of economic complexity index and renewable energy electricity

In order to contribute to the existing limited energy-environment literature, the present study analyze the carbon neutrality targets of the 16 major exporting economies while considering the role of economic complexity and renewable energy electricity consumption empirically by investigating the most recent dataset covering the period from 1990 to 2019 by employing advanced econometric techniques. This study uses the economic complexity index, connecting the country's productive structure with the amount of knowledge that the products represent. Employing various cointegration and regression techniques such as augmented mean group (AMG) and dynamic ordinary least square (DOLS) confirms the long-run cointegration among the variables such as economic growth, economic complexity, renewable energy consumption, and CO₂ emission. Also, this study provides evidence that confirms the validity of the environmental Kuznets curve hypothesis in the leading exporting economies. Regarding the carbon neutrality target, we found that both economic complexity and renewable electricity, if increase by one percent each, significantly reduce CO₂ emissions by 0.1491 (AMG) and 0.130% (DOLS) and 0.160 (AMG) and 0.203% (DOLS), respectively, that help attain the carbon neutrality target.

Ambient air pollution and meteorological factors escalate electricity consumption

The impact of climate change is evident in the variability of weather patterns, hence, affecting electricity generation and consumption. Existing literature examines the effect of humidity and temperature on energy, but suffers from omitted variable bias. Here, we adopt several parameters namely ambient air pollution, precipitation, surface pressure, dew-frost point, relative humidity, wind speed, earth skin temperature, cooling degree days, heating degree days, solar and wind generation, cumulative installed PV power, and wind turbine capacity, solar and wind electricity consumption, and energy price index to investigate the role of climatic and energy-related factors on households, industry sector, commercial and public service attributed electricity consumption in Norway. Our machine learning estimator accounts for climate change heterogeneity, and historical effects while controlling omitted-variable and misspecification bias. The empirical assessment shows the radiative forcing effect of ambient air pollution decreases electricity consumption. In contrast, the scavenging effect of rainfall intensity on ambient air pollution improves both wind and solar electricity consumption. Rising levels of earth skin temperature, and humidity increases solar and wind electricity consumption whereas dew-frost point drops temperature, and humidity to improve human comfort. Our study highlights that energy price index is critical to the adoption of solar and wind energy technologies.

Do economic openness and electricity consumption matter for environmental deterioration: silver bullet or a stake?

Developing countries are enthusiastically on the road to economic progress and economic openness, which is proved to be a silver bullet for them. However, it has put their environmental quality at stake. This study examines whether economic openness and electricity consumption matter for environmental deterioration by controlling for the influence of economic progress. For this, we have used time series frequency data of Pakistan from 1971 to 2016 and employed the state-of-the-art dynamic autoregressive distributed lag (ARDL) simulation model. The model has the advantage over traditional ARDL in determining the positive and negative environmental deterioration variations induced by economic openness, electricity consumption, and economic progress. The main findings are as follows: Firstly, electricity consumption in both long and short run positively and significantly influenced CO₂ emissions, while long-run influence exceeded that of short run. Secondly, economic progress validated an environmental Kuznets curve hypothesis and thus limited the environmental degradation. Thirdly, economic openness showed an insignificant influence on CO₂ emissions both in the long and short run. Based on research findings, it is suggested that economic progress and economic openness are not the direct culprits to deteriorate a developing country's environmental condition like Pakistan; rather, electricity consumption remained the key role player. Therefore, the transition from fossil-based electricity consumption to renewable energy consumption would provide a sustainable pathway towards achieving sustainable economic openness in the future.

Methodology for forecasting electricity consumption by Grey and Vector autoregressive models

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The Grey and Vector autoregressive models are coupled to improve their accuracy.

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Five economic and demographic parameters are included in the new hybrid model.

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This new model is a reliable forecasting tool for assessing energy demand.

Forecasting energy demand in general, and electricity demand in particular, requires the developing reliable forecasting tools that can be used to monitor the evolution of consumers’ energy needs more accurately. The proposed new hybrid GM(1,1)-VAR(1) model is meant for that purpose. The latter is based on the Grey and Vector autoregressive approaches, and makes it possible to predict future demand, by taking into account economic and demographic determinants with an exponential growth trend. With an associated APE of 1.5, a MAPE of 1.628%, and an RMSE of 15.42, this new model thus presents better accuracy indicators than hybrid models of the same nature. Also, it proves to be as accurate as some recent hybrid artificial intelligence models. The model is thus a reliable forecasting tool that can be used to monitor the evolution of energy demand.•

The Grey and Vector autoregressive models are coupled to improve their accuracy.

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Five economic and demographic parameters are included in the new hybrid model.

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This new model is a reliable forecasting tool for assessing energy demand.

Temperature change and electricity consumption of the group living: A case study of college students

The relationship between energy use and climate change is the center of analysis about mitigation and adaptation. Yet current studies of the electricity-climate relationship focus on developed countries. Little was known about the energy-use behavior in group living. By using college students' monthly electricity-use data from September 2018 to August 2019 in Beijing, China, we build a weighted least square regression model and found a U-shaped relationship between temperature and electricity consumption. The results show that one additional day of temperature exceeding 30 °C would cause a 16.8% increase in monthly electricity consumption with reference to 18-22 °C while one additional day of temperature below -6 °C will increase it by 6%. The magnitudes of temperature effect on electricity are much greater than those in Shanghai and California. Further, we find that building structures, such as windows orientation and floor height, play important roles in the temperature-electricity relationship. Finally, we predict the changes in electricity use in a collection of Representative Concentration Pathways (RCP). It finds that the electricity use in summer in north China would increase by 72.8% in RCP 4.5, 79.5% in RCP6.0, and 91.2% in RCP8.5. Our study could be extended to the urban area in northern China, and indicates how the electricity use would respond to climate change in the Beijing-Tianjin-Hebei Urban Agglomeration, covering 8.1% of China's population and 8.4% of gross domestic product. Climate change impact on electricity use in residential and commercial sectors is significant and varying in regions. To achieve sustainable and environmental-friendly development, building structures could play a more effective role in energy-saving and adaptation to climate change.

The effects of international tourism, electricity consumption, and economic growth on CO2 emissions in North Africa

This study examines the influence of tourism, economic growth, and electricity consumption on carbon dioxide (CO2) emission in the presence of the Environmental Kuznets Curve (EKC) model for a panel of four countries of North Africa, namely, Morocco, Algeria, Tunisia, and Egypt, over the period 1980-2014. Since we find the existence of cross-sectional dependence, we apply the unit root tests of CIPS and CADF, the Westerlund cointegration test as well as the dynamic seemingly unrelated regression (DSUR), and the Dumitrescu-Hurlin Granger causality test. The empirical results show that electricity consumption has a positive effect on CO2 emissions. In contrast, tourism has a negative relationship with CO2 emissions, implying improvement in the quality of the environment. The conclusions confirm the hypothesis of the environmental Kuznets curve for the countries in our sample. In addition, the causality test indicates the following results: (i) a one-way causality from real income, electricity consumption, and tourism to carbon emissions. (ii) A one-way causality running from electricity consumption to real income and tourist arrivals. (iii) A two-way causality between real income and tourism development. Based on these empirical results, several policy recommendations are proposed for the four countries of North Africa.

Environmental implications of reduced electricity consumption in Wuhan during COVID-19 outbreak: A brief study

Due to the COVID-19 outbreak, Wuhan was locked down from 23 January 2020 to 8 April 2020, a total of 76 days. It is well known that the electricity consumption is a direct reflection of human activity. During the lockdown of Wuhan, most of human activities were forbidden. The reduction in human activity would inevitably lead to a reduction in electricity consumption. At the same time, anthropogenic emissions of air pollutants would also be reduced with the reduction of human activity. In this study, the correlation between electricity consumption and air pollutants during lockdown was discussed in detail. The result showed that the drop in pollutants concentrations in January should be attributed to the washout effect of rainfall rather than the lockdown. The decrease of electricity consumption in the secondary industry might play a significant role on the decrease of PM 2.5 and NO₂ concentrations in Wuhan in February 2020. The decrease in NO₂ concentration in March should be attributed to the reduction of pollutants emissions from the tertiary industry, which means that more attention should be paid to the control of NO₂ emission in the tertiary industry. Due to reduced emissions from local sources, the role of long-range transport sources might be more significant during the lockdown of Wuhan. By PSCF analysis, southeast of Wuhan could be the major potential emission sources of PM 2.5 , especially in the northern part of Jiangxi province. It was suggested that stricter regulation of pollutants emissions should be implemented in this area.

Investigation on the main contributors of economic growth in a dynamic heterogeneous panel data (DHPD) in Africa: evidence from their income classification

In investigating the key contributors (electricity consumption, foreign direct investment, carbon dioxide emissions, and population) of economic growth in Africa, this study clustered the selected countries into their income levels spanning from 1990 to 2018. Applying the Westerlund bootstrap co-integration unveiled, the employed variables have a long-run equilibrium association. Estimates from the dynamic common corrected effects revealed that a 1% rise in electricity consumption increases economic growth by 0.187%, 0.040%, and 0.511% in upper middle income, lower middle income, and low middle income, respectively. The elasticity of carbon dioxide emissions to economic growth is high in low-income countries than in the other two groupings. In contrast, a percentage rise in foreign direct investment heightened economic growth by 0.919% and 0.154% in upper middle income and lower middle income. As the growth hypothesis was established among the panel groupings, it points out that a country's economy is energy dependent. Thus, a rise in electricity consumption in Africa will lead to a surge in economic growth since energy usage is a direct input into the manufacturing process and/or an indirect input that complements labor and capital inputs. However, its ripple effects of polluting the environment need not be overlooked. These findings imply that electricity usage and economic growth are highly corrected. These approaches consider cross-sectional reliance into consideration; thus, the empirical findings have drawn some significant policy implications.

Estimating the macroeconomic determinants of total, renewable, and non-renewable energy demands in Bangladesh: the role of technological innovations

Bangladesh is well on course to become one of the leading emerging market economies in the world. Hence, it can be expected that the economic growth of Bangladesh would substantially increase over the next decade. This, in turn, is likely to boost the energy consumption levels of the nation whereby meeting the surge in the energy demand would be a crucial agenda of the government. Therefore, it is important to understand the factors that influence the nation's energy demand. Against this backdrop, this paper aims to evaluate the macroeconomic determinants of total, renewable, and non-renewable energy demands in Bangladesh between 1980 and 2014. Besides, the analysis is conducted for both primary energy and electricity consumption levels. The econometric methods used in this study controlled for the structural break issues in the data. The key findings, in a nutshell, show that economic growth and household consumption expenditure positively influence the overall primary energy and electricity demands in Bangladesh while income inequality exerts opposite effects. Besides, technological innovations are found to be reducing the total and non-renewable energy demand in Bangladesh while increasing the demand for renewable energy. On the other hand, positive oil price shocks are found to be ineffective in influencing the renewable energy demand but slightly reducing the non-renewable energy demand. Finally, the causality estimates portray the feedback hypothesis in almost all the cases to highlight the inter-relationships between economic growth and energy demand in Bangladesh. Hence, in line with these findings several critically important policy implications are suggested for managing the overall energy demand in Bangladesh.

Does economic prosperity lead to environmental sustainability in developing economies? Environmental Kuznets curve theory

Since developing countries experience economic and environmental sustainability challenges, it is desirable digging into the linkages between economic and environmental parameters. The purpose of this work is to evaluate the existence of the environmental Kuznets curve (EKC) theory (i.e., the inverse U-shape connection between real GDP per capita and per capita carbon dioxide emissions) in the sample of 11 developing countries. By using balanced annual panel data in the period between 1992 and 2014 and two alternative estimation techniques, we explored the potential inverted U-shaped linkage between carbon dioxide emissions and real GDP per capita in the sample of interest. For analysis purposes, Pedroni and Westerlund co-integration techniques are employed. Then, fully modified ordinary least squares, pooled mean group methods are applied for long-run parameter estimations. And, the Dumitrescu-Hurlin causality approach is employed for causal directions. Firstly, this work's findings provide the supportive evidence to the inverse U-shaped linkage in the long-run, indicating that an increase in real GDP per capita and electricity consumption tends to mitigate long-run carbon dioxide emissions in the developing countries, for the whole sample. Secondly, the country-specific findings suggested the presence of EKC theory for Brazil, China, India, Malaysia, the Russian Federation, Thailand, and Turkey. It implicated that these countries are on the path of attaining environmental sustainability in the long-run. However, Mexico, Philippines, Indonesia, and South Africa failed to lend credence to the EKC theory. It manifested that these countries need to design strategies directed to reduce carbon dioxide emissions from economic activity and electricity generation through efficiency improvement or promotion of renewables. Finally, bidirectional causal links are observed among all the variables of interest. The findings suggest that country-specific targeted action plans should be implemented to ensure the environmental sustainability in the developing world.

Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach

The aim of this research is to forecast seasonal fluctuations in electricity consumption, and electricity usage efficiency of industrial sectors and identify the impacts of the novel coronavirus disease 2019 (COVID-19). For this purpose, a new seasonal grey prediction model (AWBO-DGGM(1,1)) is proposed: it combines buffer operators and the DGGM(1,1) model. Based on the quarterly data of the industrial enterprises in Zhejiang Province of China from the first quarter of 2013 to the first quarter of 2020, the GM(1,1), DGGM(1,1), SVM, and AWBO-DGGM(1,1) models are employed, respectively, to simulate and forecast seasonal variations in electricity consumption, the added value, and electricity usage efficiency. The results indicate that the AWBO-DGGM(1,1) models can identify seasonal fluctuations and variations in time series data, and predict the impact of COVID-19 on industrial systems. The minimum mean absolute percentage errors (MAPEs) of the electricity consumption, added value, and electricity usage efficiency of industrial enterprises separately are 0.12%, 0.10%, and 3.01% in the training stage, while those in the test stage are 6.79%, 4.09%, and 2.25%, respectively. The electricity consumption, added value, and electricity usage efficiency of industrial enterprises in Zhejiang Province will still present a tendency to grow with seasonal fluctuations from 2020 to 2022. Of them, the added value is predicted to increase the fastest, followed by electricity consumption.

Examining the economic impact of COVID-19 in India through daily electricity consumption and nighttime light intensity

The COVID-19 pandemic has disrupted economic activity in India. Adjusting policies to contain transmission while mitigating the economic impact requires an assessment of the economic situation in near real-time and at high spatial granularity. This paper shows that daily electricity consumption and monthly nighttime light intensity can proxy for economic activity in India. Energy consumption is compared with the predictions of a consumption model that explains 90 percent of the variation in normal times. Energy consumption declined strongly after a national lockdown was implemented on March 25, 2020 and remained a quarter below normal levels throughout April. It recovered subsequently, but electricity consumption remained lower even in September. Not all states and union territories have been affected equally. While electricity consumption halved in some, it declined very little in others. Part of the heterogeneity is explained by the prevalence of COVID-19 infections, the share of manufacturing, and return migration. During the national lockdown, higher COVID-19 infection rates at the district level were associated with larger declines in nighttime light intensity. Without effectively reducing the risk of a COVID-19 infection, voluntary reductions of mobility will hence prevent a return to full economic potential even when restrictions are relaxed. Together, daily electricity consumption and nighttime light intensity allow monitoring economic activity in near real-time and high spatial granularity.

Impacts of the COVID-19 lockdown on energy consumption in a Canadian social housing building

The COVID-19 pandemic hit societies in full force in 2020 and compelled people all around the world to change their lifestyle. The time spent at home significantly surged during the pandemic and this change in occupancy can have a direct impact on building energy consumption. COVID-19 lockdowns also accelerated the transition towards telework, a trend that many expect to last. Changes in energy consumption under lockdown is thus a valuable asset to forecast how energy could be consumed in buildings in the future. Here, we aim to quantify the impacts of the COVID-19 lockdown on the energy consumption (electricity, hot water and space heating) in residential buildings by answering these two questions: (i) Did the lockdown lead to changes in total energy consumption?, and (ii) Did the lockdown lead to changes in consumption patterns (i.e. time of the day at which energy is consumed)? To do so, we compared the energy consumption measured in a 40-dwelling social housing building located in Quebec City (Canada) during four months of lockdown to those of the months that preceded the lockdown. It is found that consumption patterns for electricity and hot water changed for the first two months of the lockdown, when the most intensive lockdown measures were applied. Overall consumption slightly increased for these two energy expenditures, but the more important change was that consumption occurred throughout the day instead of being concentrated in the evening as observed before the lockdown. Results shed light on the impact of lockdown on energy bills for consumers and on how energy utilities might be solicited during this kind of episode.