This study examined the long-run and short-run heterogeneous links among urban concentration, non-renewable energy use intensity, economic development, and environmental emissions index across the ...regional development levels of 31 Chinese provinces. By employing the augmented mean group method and Dumitrescu-Hurlin causality, the following results are drawn: Firstly, a bidirectional positive linkage was existent between the economic development and urban concentration in both the long-run and short-run across regional development levels. Secondly, a unidirectional positive linkage emerged from non-renewable energy use intensity to environmental emissions index, with the most influential effect in EER China (highest development level). Thirdly, bidirectional mixed linkages prevailed between economic development and non-renewable energy use intensity. Economic development mitigated the non-renewable energy use intensity (inverted U-shaped curve) in the national data set and EER China (highest development level); nevertheless, the linear linkage was observed in IER China (medium development level) and WER China (lowest development level). Fourthly, unidirectional mixed linkages were found from urban concentration to non-renewable energy use intensity and environmental emissions index. Urban concentration demonstrated a U-shaped linkage with non-renewable energy use intensity and environmental emissions index in the national data set and EER China. But it unveiled a linear linkage with both variables in IER China and WER China. Fifthly, economic development showed an environmental Kuznets curve with environmental emissions index in the national data set and EER China. Conversely, it showed a linear linkage with the environmental emissions index in IER China and WER China. In turn, the environmental emissions index linearly hampered the economic development in the national data set as well as regional samples. Finally, the long-run and short-run effects showed homogeneity of the linkages' nature; yet, the degree of effects in the long-run surpassed those in the short-run for all development levels.
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•Employed Principal Component Analysis to estimate environmental emissions index (EEI)•Environmental Kuznets Curve (EKC) hold at national as well as highest development levels.•Urban concentration (UC)/economic development (ED) had inverted U-shaped link with EEI.•Medium/lowest development regions (non-EKC) had positive linear links of UC and ED with EEI.•Degree of long-run effects exceeded short-run for all models at all development levels.
•Energy use positively affects carbon emission intensity.•Urbanization has positive impact on carbon emission intensity.•Technological innovation negatively affects carbon emission intensity.•Data ...was analyzed by Quantile-on-quantile model.
In recent years, policy experts have engaged in a spirited discussion over the relationship between environmental quality and energy use. This association is critical for the mitigation of climate change and achieving Sustainable Development Goals (SDGs). Furthermore, current studies have not given enough consideration to this relation, notably in the case of the Kingdom of Saudi Arabia (KSA). Therefore, this study examines the KSA case, focuses on the connection of industrialization, foreign direct investments, and technological innovation with carbon emissions intensity (CEI) by also considering urbanization and energy use, examines the period between 1991 and 2020, and uses quantile-on-quantile (QQ) regressionand quantile regression (QR) approaches for empirical examination. The empirical outcomes revealthat (i) urbanization and energy usehave an increasing impact on CEIin all quantiles; (ii) industrialization, foreign direct investments, and technological innovation have a generally decreasing impact on the CEI in most quantiles; (iii) power of the factors’ impact on the CEI change according to quantiles; (iv) the robustness the findings is also validated. Hence, theresults highlight the importance of quantile-based analysis and suggest that KSA authorities need to embrace environment-friendly policies while managing environmental pollution to develop industrialization level and technological innovation as well as increase foreign direct investments.
Previous studies have considered the effect of financial development on carbon emissions; however, few studies have explored the role of green finance in carbon mitigation. To bridge this gap, the ...current study constructs a green finance development index based on four indicators: green credit, green securities, green insurance, and green investment. A vector error correction model is used to analyze relationships between the development level of green finance, non-fossil energy consumption, and carbon intensity using data from 2000 to 2018. We find that China’s green finance industry developed rapidly, and improvements in the green finance development index, as well as the increasing use of non-fossil energy, contributed to a reduction in carbon intensity. Simultaneously, an increase in carbon intensity inhibited the expansion of non-fossil energy use, impeded the investment flow to green projects, and ultimately led to a deterioration of green finance development. In addition, non-fossil energy consumption in China was primarily influenced by green finance and carbon intensity, with clear policy-driven effects. However, the impacts of green finance policies continually fell short and lacked continuity. This study proposes ways in which to improve the effect of green finance policy implementation, expand the consumption of non-fossil energy, and develop a carbon trading market.
•The green finance–new energy use–carbon intensity nexus is explored for China.•A vector error correction model is employed.•A green finance development index is constructed based on four green indicators.•Green finance and new energy use are found to reduce carbon intensity.•Carbon intensity increases are found to inhibit green finance and new energy use.
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•A novel analysis of the effect of LEDs on the greenhouse energy budget is presented.•The total energy system, including heating and lighting demand, was examined.•Energy savings in ...multiple climate scenarios from around the world were examined.•Energy for light was reduced by 40% while energy for heating increased by 9–49%•A transition to LEDs was predicted to save 10–25% of total greenhouse energy demand.
Greenhouses in high latitudes consume vast amounts of energy for heating and supplemental lighting. Light emitting diodes (LEDs) have been suggested as having great potential for reducing greenhouse energy use, as they are extremely efficient at converting electricity to light. However, LEDs emit very little heat, which must be compensated by the greenhouse heating system. Thus, it is unclear how much energy can be saved by LEDs when the need for extra heating is taken into account. This study presents a first analysis of the energy demands for greenhouses transitioning from high-pressure sodium (HPS) to LED lighting, providing a quantification of the total energy savings achieved by LEDs. Model simulations using GreenLight, an open source greenhouse model, were used to examine a wide range of climates, from subtropical China to arctic Sweden, and multiple settings for indoor temperature, lamp intensity, lighting duration, and insulation. In most cases, the total energy saving by transition to LEDs was 10–25%. This value was linearly correlated with the fraction of energy used for lighting before the transition, which was 40–80%. In all scenarios, LEDs reduced the energy demand for lighting but increased the demand for heating. Since energy for lighting and heating is often derived from different origins, the benefits of a transition to LEDs depend on the environmental and financial costs of the available energy sources. The framework provided here can be used to select lighting installations that make optimal use of available energy resources in the most efficient and sustainable manner.
The density matrix quantum Monte Carlo (DMQMC) method is used to sample exact-on-average N-body density matrices for uniform electron gas systems of up to 10^{124} matrix elements via a stochastic ...solution of the Bloch equation. The results of these calculations resolve a current debate over the accuracy of the data used to parametrize finite-temperature density functionals. Exchange-correlation energies calculated using the real-space restricted path-integral formalism and the k-space configuration path-integral formalism disagree by up to ∼10% at certain reduced temperatures T/T_{F}≤0.5 and densities r_{s}≤1. Our calculations confirm the accuracy of the configuration path-integral Monte Carlo results available at high density and bridge the gap to lower densities, providing trustworthy data in the regime typical of planetary interiors and solids subject to laser irradiation. We demonstrate that the DMQMC method can calculate free energies directly and present exact free energies for T/T_{F}≥1 and r_{s}≤2.
Although several research studies have adopted specific energy consumption (SEC) as an indicator of the progress of improved energy efficiency, publications are scarce on critical assessments when ...using SEC. Given the increasing importance of monitoring improved industrial energy efficiency and the rising popularity of SEC as an energy key performance indicator (e-KPI), an in-depth analysis and problematization on the pros and cons of using SEC would appear to be needed. The aim of this article is to analyse SEC critically in relation to industrial energy efficiency. By using SEC in the pulp and paper industry as an example, the results of this exploratory study show that although SEC is often used as an e-KPI in industry, the comparison is not always straightforward. Challenges emanate from a lack of information about how SEC is calculated. It is likely that SEC is an optimal e-KPI within the same study, when all deployed SECs are calculated in the same way, and with the same underlying assumptions. However, before comparing SEC with other studies, it is recommended that the assumptions on which calculations are based should be scrutinized in order to ensure the validity of the comparisons. The paper remains an important contribution in addition to the available handbooks.
•First look into energy consumed in unoccupied households using field-collected data.•The average dorm room consumed 30.2% of all electrical energy use while vacant.•Household energy use while ...unoccupied ranged from under 4% to over 80%.•No relationship found between energy consumption and percentage use while in vacant.
In previous literature, it has been reported that over 50% of all energy consumed in buildings occurs during non-working hours. Unfortunately, to the best of the authors’ knowledge, studies to date using field-collected data have only investigated energy consumed during periods of non-occupancy in non-domestic buildings and little is known regarding this quantity of energy in households. The work presented in this paper bridges this information gap and contributes to the literature by presenting a first look into this quantity in households. In a yearlong investigation of seven dormitories, hourly occupancy data and electricity consumption is combined and analyzed using numerous statistical methods to discover the amount of energy that is expended in unoccupied households. Across the seasons, the average household consumed between 27.5% and 31.5% of all energy while unoccupied. This quantity in individual rooms fluctuated from under 4% to over 80%. Differences in occupant behavior, along with time spent unoccupied, can explain the differences in this quantity among the households. Further, no meaningful relationship was found between total energy consumption and percentage of energy spent in empty rooms for individuals. High and low energy users both spent electricity while away from home in proportion to his/her consumption.
Significance Many case studies of specific cities have investigated factors that contribute to urban energy use and greenhouse-gas emissions. The analysis in this study is based on data from 274 ...cities and three global datasets and provides a typology of urban attributes of energy use. The results highlight that appropriate policies addressing urban climate change mitigation differ with type of city. A global urbanization wedge, corresponding in particular to energy-efficient urbanization in Asia, might reduce urban energy use by more than 25%, compared with a business-as-usual scenario.
The aggregate potential for urban mitigation of global climate change is insufficiently understood. Our analysis, using a dataset of 274 cities representing all city sizes and regions worldwide, demonstrates that economic activity, transport costs, geographic factors, and urban form explain 37% of urban direct energy use and 88% of urban transport energy use. If current trends in urban expansion continue, urban energy use will increase more than threefold, from 240 EJ in 2005 to 730 EJ in 2050. Our model shows that urban planning and transport policies can limit the future increase in urban energy use to 540 EJ in 2050 and contribute to mitigating climate change. However, effective policies for reducing urban greenhouse gas emissions differ with city type. The results show that, for affluent and mature cities, higher gasoline prices combined with compact urban form can result in savings in both residential and transport energy use. In contrast, for developing-country cities with emerging or nascent infrastructures, compact urban form, and transport planning can encourage higher population densities and subsequently avoid lock-in of high carbon emission patterns for travel. The results underscore a significant potential urbanization wedge for reducing energy use in rapidly urbanizing Asia, Africa, and the Middle East.
•A data mining-based energy use prediction model is developed with consideration of occupancy-related characteristics in buildings.•The prediction accuracy is significantly improved through ...accounting for diverse occupancy and its correlation with energy use.•The developed model provides acceptable prediction accuracy using minimal historical data.
Accurate predictions of energy consumption are essential to optimizing building energy use performance. To date, substantial efforts have been undertaken to improve prediction accuracy, specifically while focusing on occupants’ presence in buildings. Unfortunately, two significant obstacles remain when predicting building energy consumption using occupancy data. First, occupancy diversity among end-user groups is rarely considered during model development. Second, occupancy’s correlation with energy consumption may be weak due to variances in occupant behavior. Therefore, this research aims to investigate how occupancy-related characteristics of end-user groups affect prediction performance. In order to achieve this objective, a data mining-based prediction model is constructed to mimic building thermal behaviors. The experimental results using the proposed prediction model make it evident that prediction accuracy is improved when considering diverse occupancy and its correlation with energy use. In addition, significant prediction accuracy is achieved using only a minimal amount of historical data. With the proposed prediction model, it is possible to obtain more detailed information about energy use patterns (e.g., load shape, the amount of energy use) for end-user groups. Thus, facility managers will be able to personalize the operation of energy-consuming equipment depending on end-user group for reducing energy consumption without compromising occupants’ thermal comfort.
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