Search

The purpose of this paper is to estimate industry-specific direct rebound effects and to relate these effects to industry energy efficiency programs. The rebound effect represents economic behavior that will offset energy savings from energy efficiency improvements. The paper focuses on four energy intense sectors in Sweden; pulp and paper, iron and steel, chemical, and mining, during 2001-2012. We apply a factor demand model that allows for asymmetric energy price responses, i.e. that firms respond differently to increasing and decreasing energy prices. The results show considerable rebound effects. For electricity and non-fossil fuels, efficiency improvements could even 'backfire'. To mitigate this effect, policies, such as voluntary energy efficiency programs, should be combined with an increase in energy taxes if the ambition is to reduce overall energy use. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is one of the most effective ways to address environmental constraints and achieve high-quality economic development. The existing literature suggests that technological progress has an important impact on energy efficiency, but ignores the role of different paths of technological progress. Herein, the impact of different technological progress paths on energy efficiency was analyzed using the dynamic panel data model and the threshold model based on the energy efficiency measurement using the Meta-frontier and Nonradial Directional Distance Function model. The findings of this study were as follows: (1) During the study sample period, a fluctuating upward trend was observed in China's energy efficiency, and there were significant differences in energy efficiency in different regions of China. (2) The energy efficiency in China could be significantly improved by domestic innovation and regional technology diffusion. Even though the imported technologies do not play a significant role in promoting energy efficiency and have no positive effect on the improvement of energy efficiency from the perspective of domestic technology absorption or foreign technology absorption. (3) The eastern and central regions of China improve their energy efficiency through domestic innovation. However, none of the technological progress paths in the western and low-energy-efficiency regions have played a positive role in promoting energy efficiency. (4) The effect of promoting domestic innovation and regional technology diffusion on energy efficiency is gradually increasing with human capital improvement, and the restriction of foreign technology import on energy efficiency has obviously decreased. In a nutshell, different paths of technological progress have differential effects on energy efficiency improvements. Based on the above results, this paper makes some targeted policy recommendations on the choice of technological progress paths to improve energy efficiency in different regions of China. [ABSTRACT FROM AUTHOR]

Energy supply and environmental capacity are important material foundations for social and economic development and constraints for scientific and sustainable development, and with achieving the strategic goals of carbon peak and carbon neutral at an early date, too. Therefore, the international community generally regards energy-economic- environment (3E) as a necessary research direction. This paper studies the relationship among effective energy utilization, economic growth protection and ecological environment protection by establishing the contribution rate model of energy consumption factors to economic GDP growth. At the same time, this paper puts forward the government needs to use carbon trading market, carbon tax, such as low carbon management mechanism based on the Pareto Optimality of carbon trading market, prompts the excess money of the country or the region back to actively invest in low-carbon production, lets more energy companies to clean energy and low carbon production transformation, and helps to achieve the goal of regional energy industry carbon neutral. [ABSTRACT FROM AUTHOR]

This paper investigates fair (i.e., envy-free and efficient) allocations in an overlapping generations economy without production and with two-period lived agents. We show that there is a conflict between no-envy and efficiency when all generations have identical preferences. This conflict crucially depends on the size of the given young age consumption of the initial old generation relative to the young age consumption at the golden-rule. We then show that there exist non-stationary preferences for which such a conflict does not arise, regardless of the young age consumption of the initial old generation. JEL Classifications: D60, D63, D70 [ABSTRACT FROM AUTHOR]

This paper investigated the dynamics between trade openness and energy consumption in five members of the Association of Southeast Asian Nations (ASEAN) and three East Asian countries from 1980 to 2014. Previous theoretical discussions and empirical findings pointed out that trade openness had ambiguous effects on energy consumption. A panel dataset involving energy consumption per capita, trade openness, real gross domestic product per capita, gross fixed capital formation per capita and foreign direct investment was estimated. The Lagrange multiplier (LM) cross-sectional dependence test confirms the existence of cross-sectional dependence among the cross-sectional units. Moreover, the Westerlund cointegration test showed that the variables were not cointegrated. Hence, the dynamics among the variables were explored using the panel vector autoregressive method. The results suggested that there is a unidirectional Granger causality from trade openness to energy consumption. Further, the impulse response functions implied that the effect of trade openness on energy consumption occurred via the dynamics between trade openness and gross capital formation per capita. Finally, the forecast error variance decomposition showed that the contribution of trade openness on energy consumption increased during the examined period. [ABSTRACT FROM AUTHOR]

This conceptual paper proposes that all methodologies create a footprint like the carbon footprint. Design and implementation of new methodologies require limited resources and funding, and these resources are not equitably distributed on a global scale. Thus, we argue for more ecological uses of methodologies, especially in the context of data collection and interdependent relations of knowledge/information creation. Like the excessive use of energy sources, potentially unnecessary productions of new data, information, and evidence should not be regarded as unproblematic, let alone virtuous. Rather, qualitative researchers, funding agencies, and other bodies that evaluate research, should question whether new data, information, evidence are needed and at what cost. We also propose more data recycling, data sharing, open access data, and other ecological ways of supporting shared knowledge and monitoring excessive data production. [ABSTRACT FROM AUTHOR]

Smart homes are at the forefront of sustainable living, utilizing advanced monitoring systems to optimize energy consumption. However, these systems frequently encounter issues with anomalous data such as missing data, redundant data, and outliers data which can undermine their effectiveness. In this paper, an artificial neural network (ANN)-based approach for data imputation is specifically designed to deal with the anomalies in smart home energy consumption datasets. Our research harnesses the power of ANNs to model intricate patterns within energy consumption data, enabling the accurate imputation of missing values while detecting and rectifying anomalous data. This approach not only enhances the completeness of the data but also augments its overall quality, ensuring more reliable results. To evaluate the effectiveness of our ANN-based imputation method, comprehensive experiments were conducted using real-world smart home energy consumption datasets. Our findings demonstrate that this approach outperforms traditional imputation techniques like mean imputation and median imputation in terms of accuracy. Furthermore, it showcases adaptability to diverse smart home scenarios and datasets, making it a versatile solution for improving data quality. In conclusion, this study introduces an advanced data imputation technique based on ANNs, tailor-made for addressing anomalies in smart home energy consumption data. Beyond merely filling data gaps, this approach elevates the dataset's reliability and completeness, thereby facilitating a more precise analysis of energy consumption and supporting informed decision-making in the context of smart homes and sustainable energy management. Ultimately, the proposed method has the potential to contribute considerably to the ongoing evolution of smart home technologies and energy conservation efforts. [ABSTRACT FROM AUTHOR]

Promoting high-quality economic development, boosting high-level ecological environment protection, and integrating deeply digital economy and energy efficiency has become a general trend. This paper, based on 284 prefecture-level Chinese cities from 2008 to 2018, uses the entropy weight method and super-efficient SBM-DEA model to measure the digital economy and energy efficiency (decompose into energy pure technical efficiency and energy scale efficiency), respectively. After the spatial and temporal evolution characteristics, a fixed-effect model is constructed to study the impact of the digital economy on energy efficiency. The research results show that: (1) the development of the digital economy effectively promotes energy efficiency, mainly reflected in the improvement of pure technical efficiency of energy; (2) the development of both the digital economy and the energy efficiency in China has complex spatial and temporal evolutionary characteristics, and there is heterogeneity in the impact of the digital economy on energy efficiency at the temporal and spatial levels; and (3) digital economy effectively improves energy efficiency by reducing unnecessary energy consumption through the R&D innovation effect. The development of the digital economy has far-reaching significance on the optimization and upgrading of the economic system, accelerating the improvement of energy efficiency and achieving green transformation. [ABSTRACT FROM AUTHOR]

The globe is now in ecological turmoil as a result of the unrelenting increase in global warming. As a result, governments worldwide are committing to decarbonizing the environment, with the United Arab Emirates (UAE) and the Kingdom of Saudi Arabia (KSA) playing an important role in this effort. Hence, this paper evaluates the nonlinear (asymmetric) impact of natural gas consumption on renewable energy consumption and economic growth in the KSA and the UAE utilizing data stretching from 1990 to 2020. The study also considers other drivers of renewable energy consumption and economic growth, such as trade openness and CO2 emissions. The study utilizes nonlinear autoregressive distributed lag (ARDL) to evaluate these associations. The outcomes of bounds nonlinear ARDL (NARDL), affirm the long-run association between the variables in both countries. The nonlinear results show that positive and negative shocks in natural gas consumption have a negative impact on renewable energy in both UAE and KSA. In contrast, positive and negative shocks in natural gas consumption impact economic growth positively. The study proposed vital policy recommendations based on these results. [ABSTRACT FROM AUTHOR]

Investigating the current and future dynamics of energy consumption in modern economies such as the UK is crucial. This paper predicts the UK's energy consumption using data spanning January 1995 to March 2022 by comparing and evaluating the forecast performance of machine learning, dynamic regression, time series and combination modelling techniques. The analysis reveals that the seasonal ARIMA and TBATS hybrid models yield the lowest forecast errors in predicting the UK's electricity and gas consumption. Although the combination forecasts performed poorly relative to other models, machine learning techniques such as neural network and support vector regression produced better results compared to the dynamic regression models, whereas the seasonal hybrid model performed better than the machine learning and time series models. The results indicate that the UK's electricity consumption would either stabilise or decline over the forecast horizon, suggesting that it will take some years for electricity consumption to attain pre-2019 levels. For gas consumption, the results indicate that consumption would either maintain current levels or increase over the forecast period. We also show that combination forecasts do not often generate the best predictions, and therefore, choice of methodology matters in energy consumption forecasting. Overall, changing seasonal patterns, energy efficiency improvements, shift to renewable sources and uncertainties due to the COVID-19 pandemic, Brexit, and the Russia–Ukraine crisis appear to be significant drivers of energy consumption in the UK in recent times. These findings are expected to help in designing more effective energy policies as well as guide investor decisions in the energy sector. [ABSTRACT FROM AUTHOR]

Energy efficiency is the key to green development, and the government plays a vital role in energy efficiency. This paper clarifies the mechanism by which the Yangtze River Delta Economic Coordination Committee affects the energy efficiency of urban agglomeration by promoting market integration. Based on panel data of China's prefecture-level cities from 2004 to 2017, we take the Yangtze River Delta Economic Coordination Committee as a quasi-natural experiment of government cooperation and use the difference-in-difference method to test whether this organization has enhanced the energy efficiency of urban agglomeration. The results show that the Yangtze River Delta Economic Coordination Committee can significantly improve energy efficiency in urban agglomerations. The mechanism analysis shows that it reduces the energy consumption per unit of gross domestic product by enhancing the marketization level, perfecting the relationship between the government and the market, and improving the factor market development. The heterogeneity analysis shows that cities with lower city size, lower level of innovation, and cleaner industrial structures gain more benefits in energy efficiency from government cooperation in urban agglomeration. This paper provides empirical evidence for cities to realize integrated energy conservation through government cooperation and market integration. [ABSTRACT FROM AUTHOR]

In this paper, we have investigated the implications of the threshold effect of changes in FDI inflows for the nexus between energy consumption and economic growth in eight under-researched sub-Saharan African countries for the period 1971--2016. The countries are Benin, Congo, Kenya, Nigeria, Senegal, South Africa, Sudan, and Zambia. Using the lag-augmented VAR (LAVAR) model (corrected for cross-sectional dependence), we develop an empirical framework tightly linked to the endogenous growth model that allows for a threshold effect of changes (strength and weakness) in FDI inflows on the nexus. Our findings show that the FDI inflows matter for the causal link between energy consumption and economic growth in some countries, although, for the cross-section as a whole, our bootstrap simulation supports the neutrality hypothesis. The overall results suggest that an energy demand policy, such as an energy conservation policy, should not cause any significant adverse side-effects to economic growth in those sub-Saharan African countries. Policy implications of the threshold effect for the nexus for individual sub-Saharan African countries are also provided. [ABSTRACT FROM AUTHOR]

In line with the importance of SDG-7, a number of studies have endeavored to divulge the changes in renewable energy consumption (REC); however, the literature fails to either understand the importance of technology i.e., information communication technologies (ICT) and macroeconomic uncertainty in this context or employ robust econometric techniques. This research paper extends the prior literature by focusing on technology and macroeconomic uncertainty as novel determinants in addition to natural resources, human development, globalization, and economic growth as control variables of renewable energy for the top 10 renewable energy-consuming countries by applying several second and third generation econometric tests on annual data from 1990 to 2017. The empirical estimations determine ICT as a crucial factor of renewable energy, suggesting that it significantly triggers REC in the top economies. Conversely, the detrimental effects of uncertainty tend to shrink REC. Furthermore, natural resources, human development, globalization, and economic growth significantly boost REC as consistent with the existing literature. Based on these findings, this study suggests several SGD-oriented policies. [ABSTRACT FROM AUTHOR]

This paper is concerned with the impact on the global greenhouse effect issues and focus on comprehensive multi-indicator though analyze the relationship between CO2 emissions and real GDP, energy intensity, fraction of the renewable energy supply out of the total energy supply, and energy consumption from different sectors, including the agricultural, service, industrial and transport sectors. The panel data encompass all 37 Organization for Economic Co-operation and Development (OECD) member countries covering 20 years from 2000 to 2019 and divide into two subsamples, the first and second ten years, for comparison. The empirical results of all parameters track the sound progress of green growth trend in OECD countries in the second ten years (2010–2019). According to the OECD, green growth strategy had been acknowledged in OECD Ministerial Council Meeting in June 2009. Thus, the study divided into the first and second ten years with 2010 as the boundary to compare and the empirical result also indicates the green growth strategy in OECD countries have gradually matured in the last ten years. The implementation of the policy framework and measures includes pricing, removing perverse subsidies, ensuring the regulatory standards of energy efficiency, innovation, and infrastructure investments might also provide a reference and important information for energy policy makers. [ABSTRACT FROM AUTHOR]

The increasing demand, rapid consumption, price increase, limited reserves, and environmental concern due to pollution produced by conventional fossil fuel (diesel & gasoline) are a few reasons why biofuels need to be explored. The present paper employs a systematic methodology to examine the performance of a 20% volumetric blend of Sal biodiesel (S20) blended with diesel using α-aluminium oxide (α-Al2O3) nanoparticles (NP) as additives and is compared with a diesel under like circumstances. The central composite design, Box-Behnken design (BBD) based response surface methodology, and desirability tests are used in the organized experiments on a diesel engine configuration to facilitate calibration. The created multivariate regression model yields all of the best engine inputs. Interaction effects are used to determine the most influential element by observing the interaction of two distinct input factors on a single response. According to the desirability tests, the highest estimated desirability was 0.579; the optimal input parameters found are 21°bTDC injection timing (IT), 238 bar injection pressure (IOP), 17 compression ratio (CR), and 74 ppm concentration of α-Al2O3NP, estimated the optimized response of brake thermal efficiency (BHTE) 31.18%, brake specific fuel consumption (BSFC) 0.2975 kg/kWh, carbon monoxide (CO) 0.0887%, hydrocarbon (HC) 31 ppm, oxide of nitrogen (NOx) 677 ppm, and smoke level 54.92%. These predicted values were validated with experimental results, and errors were within the range. The nanoparticle combination sample offers improved brake thermal efficiency (BTHE) and lower BSFC rate than the S20 while testing for the optimal parametric condition. Graphical abstract [ABSTRACT FROM AUTHOR]

Expanding the supplies of new energy can not only reduce CO2 emissions, but also alleviate energy shortage. This paper applies the quantile regression to investigate the new energy industry in China. The results show that economic growth exerts the greatest effect on the new energy industry in the lower 10th quantile province. This is because these provinces have the developed economies, demand for a higher ecological environment and new energy resources. Foreign energy dependence has a minimal impact on the new energy industry in the 25th-50th quantile province, due to their minimal oil importation. The contribution of technological progress to the upper 90th quantile province is the lowest, because their R&D capabilities are the weakest. The impact of energy consumption structure decreases in steps from the lower 10th quantile provinces to the upper 90th quantile provinces. The agricultural sector promotes the new energy industry in most provinces. [ABSTRACT FROM AUTHOR]

This paper examines the determinants of inequality in the distribution of CO2 emissions across U.S. regions. We implement a factorial decomposition of CO2 per capita based on extended Kaya factors, that is, carbon intensity of fossil fuel consumption, energy mix, energy intensity of GDP, economic growth in terms of labor productivity and employment rate. Results reveal that U.S. states display marked differences in most factors. We identify energy intensity as the main source of emissions inequality. Based on the within and between group inequality components we also explore the effect of geographical, geological, climatic and human development partitions of U.S. states' groups. Findings indicate that the within-group inequality had been the main contributor to the whole inequality. Finally, some economic policy implications are also discussed; explaining the unequal distribution of emissions is vital to establish differentiated targets and work towards successful mitigation proposals. [ABSTRACT FROM AUTHOR]

For a few decades, factors affecting environmental deterioration have been at the center of much interest This paper examines the impact of income level, disaggregated energy consumption, types of globalization level, and urbanization on per capita ecological footprint by utilizing novel machine learning techniques (tree regression, boosting, bagging, and random forest) for 27 OECD countries during 1971–2016. It is found that the random forest algorithms best fit the dataset. The empirical results exhibit that oil product consumption, electricity consumption, and gross domestic product are the most significant variables for our model. Besides, the partial dependence plots results show that economic growth and especially fossil fuel energy consumption damage the environment. These findings have important implications for both developed and developing countries for designing proper energy and environmental policies. Especially, policymakers should focus on sustainable development instead of plain economic growth. [ABSTRACT FROM AUTHOR]

By developing the mathematical model of a serial multi-stage reverse electrodialysis reactor (REDR) system for wastewater treatment, this paper numerically simulates the degradation process of azo dye (methyl orange) dyeing wastewater. The operation performances of the serial system with an anode and cathode synergetic degradative circulation mode are explored by numerical simulation. The influences of operation parameter variations on key performance indicators are investigated and discussed. Results indicate that the serial system can achieve excellent electricity conversion efficiency and degradative performance under an appropriate output current condition. A high initial MO concentration and electrode rinse solution (ERS) flowrate are helpful to improve the treatment performance of the system. A low concentrated solution (CS) concentration is beneficial for raising the electricity conversion efficiency and reducing the total energy consumption (TEC). [ABSTRACT FROM AUTHOR]

During Covid-19 pandemic world economy experienced negative growth rate, therefore energy consumption and consequently emission pollution decreased. According to Environmental Kuznets Curve, it is expected that energy consumption and emission pollution increase in response to Covid-19 economic recovery, even higher than its pre-pandemic level. The goal of this paper is to study the environmental risk of Covid-19 economic recovery. We use an Environmentally-Augmented Global Vector Autoregressive Model (E-GVAR) to trace dynamic effects of Covid-19 economic recovery on pollution emission. Using generalized impulse response functions (GIRFs), we investigated the effect of positive economic shocks in real per capita income in China and USA economies on total C O 2 equivalent emission pollution. The results show that positive economic recovery affects emission pollution significantly. China and emerging economies may experience high risk while Europe region is moderately affected by this positive shock. A positive Economic Shock in China decrease pollution emission in USA over time. It can be attributed to substitution effect of Chinese product in global market. Generally, our results demonstrate spillover effect of transition shocks from large economies to the rest of world and highlights the importance of linkages in the world economy. [ABSTRACT FROM AUTHOR]

This paper uses data from the 2015 Residential Energy Consumption Survey to explore the extent to which renters' electricity use in the United States exceeds that of otherwise similar non-renters. Renting households are found to use approximately 9% more electricity than non-renters when controlling for location, socioeconomic, and many appliance-quantity controls. There are multiple factors that explain this extra electricity use, including inferior energy efficiency of appliances, behavioral factors, differences in bill payment responsibilities, and additional reliance by renters on electric space and water heaters. The paper finds that none of these factors are dominant. The phenomenon of renters' (conditionally) higher electricity use is thus best understood as one that emerges from multiple sources. [ABSTRACT FROM AUTHOR]

The optimal power flow is one of the major area in economic and efficient dispatch of electric power. This research article presents a swarm-based optimization algorithm, known as Modified Ant Lion Optimization (MALO) algorithm to solve optimal power flow (OPF) problems. A holistic approach has been used including thermal, wind, solar, and hydro power plants and results shows minimization of cost, losses, and voltage deviation that amounts for novelty of this paper. The MALO algorithm is validated on IEEE 30-bus and IEEE 57-bus systems, and the result are compared with the state-of-the-art algorithms. It is found that proposed algorithm provides better OPF solutions when compared with other mentioned algorithm namely Antlion Optimization, Graw Wolf Optimization, Salp Swarm Algorithm, and Grasshopper Optimization. The MALO algorithm is useful for electric utilities, researcher, and power system operation. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of women's intra-household bargaining power on household cooking fuel choice. It further analyzes the determinants of household energy spending after the decision to use a given fuel has been made. The results reinforce the important role women play in the household cooking fuel choice and energy transition from traditional to modern fuel. In addition, income and education are found to be crucial factors that influence both household cooking fuel choice and energy expenditure. These findings imply that energy transition policies need to consider gender dimension and women's intra-household bargaining power. [ABSTRACT FROM AUTHOR]

China has proposed ambitious goals of carbon peak and carbon neutrality, and will pay more attention to the relationship between economic development and carbon emissions. It's significant to assess the current environmental decoupling and prospect the future conditions in China. This article studies the decoupling status and influencing factors in Chinese provinces from 1996 to 2018 through Tapio decoupling index and decomposition model. The results show that most provinces are currently in a weak decoupling state. The growth of per capita GDP and population will affect the process of strong decoupling, while the optimization of energy intensity, energy structure and industrial structure will promote the realization of strong decoupling. Moreover, this paper prospects the decoupling scenarios of Chinese provinces from 2019 to 2035 and finds that all provinces will achieve strong decoupling before 2040, 2035 and 2030 under different carbon emissions scenarios respectively. To achieve the strong decoupling of economic output and carbon emissions as soon as possible, the government must improve energy efficiency, use renewable energy in large quantities, and promote the industrial transformation and upgrading. [ABSTRACT FROM AUTHOR]

The global rise in energy consumption makes managing energy demands a priority. Here, the potential of Information and Communication Technology (ICT) in controlling energy consumption is still debated. Within this context, the main objective of the current study is to measure the impact of ICT, its potential benefit, and environmental factors on household electricity demand in Taiwan. A panel of data from 20 cities in Taiwan was collected during the period 2004–2018. We adopted PMG estimation and applied the DH-causality test for analysis. The estimation results show that ICT, carbon emissions, and climate change will drive household electricity demand in Taiwan in the long term. However, ICT has a higher potential to reduce electricity demand in the short-term period. In addition, the results of the causality test reveal a two-way interrelationship between ICT and electricity demand. Our study also found that climate change indirectly affects the use of electricity through household appliances. We also presented several policy implications at the end of this paper. [ABSTRACT FROM AUTHOR]

This paper examines the causation between economic growth, tourism import, industrialization, renewable energy, non-renewable energy use, trade openness, and environmental sustainability which is proxied by carbon emissions for 8 Asian countries (China, Japan, India, Indonesia, South Korea, Philippines, Thailand, and Vietnam) over 20 years. Causal relations were tested using Pooled Mean Group Autoregressive distributive lag model (PMG-ARDL) and Dumitrescu and Hurlin's (2012) panel granger causality test The PMG-ARDL model results reveal that in the long-run renewable energy usage, economic growth, and trade have a significant negative influence on the emission of carbon, while non-renewable energy usage, tourism import, and industrialization exhibit a significant positive impact on CO2 emissions of the sampled Asian countries. In the short run, renewable energy has a significant negative influence on CO2 emissions. While economic growth exhibit a significant positive influence on carbon emissions in the short-run. Furthermore, the Granger causality analysis reveals that there is a feedback mechanism between industrialization, tourism import, non-renewable energy, renewable energy, and CO2 emissions meaning that the future dynamics of carbon emissions in the sampled countries can be significantly explained by industrialization, tourism import, renewable energy, and non-renewable energy. Contrarily, trade and economic growth are good to explain the dynamics of carbon effusion of the sampled Asian countries in the future but without feedback. It is recommended that policymakers in Asian countries should formulate stringent environmental policies that will encourage industries in these countries to utilize clean energy sources so that economic growth will be achieved simultaneously with carbon neutrality. [ABSTRACT FROM AUTHOR]

Many economists and psychologists have studied the impact of nudges on households' pro-environmental behaviors. Interestingly, "private nudges" can be imagined for companies. Yet, studies focusing on nudging employees' energy use are rare. The objective of our paper is to explore this issue with the help of a field experiment conducted at 47 French companies' sites. Using a difference-in-difference methodology, the effects of three nudges on employees' energy conservation are tested. The first nudge, "moral appeal", stresses the responsible use of energy. The second one, "social comparison", informs employees on the energy consumption of other firms participating in the experiment. Finally, the third nudge, "stickers", alerts employees about good energy conservation practices. Our results stress the complementarity of these nudges. When implemented alone, the three nudges have no significant effects on energy consumption. However, when the moral appeal and social comparison nudges are combined with the stickers nudge, they become effective. [ABSTRACT FROM AUTHOR]

The integration of distributed generation (DG) units such as wind power into the distribution network are one of the most viable technique to meet the energy demand increases. But, the integration of these DG units into power systems can change the dynamic performances of the systems and create new challenges that are necessary to be taken care of in the operation of the network. The fault location and diagnosis are the most significant technical challenges that can improve power systems' reliability and stability. In this paper, a Multi-Agent System (MAS) based on current amplitude and current direction measured proposed for fault location, isolation, and power restoration in a smart distribution system with the presence of a wind power generator. The agents can communicate and collaborate to locate the faulted line, then send trips signal to corresponding circuit breakers accordingly. The simulation results show the performance of the proposed techniques. [ABSTRACT FROM AUTHOR]

If the designers of technologies intended to reduce or shift energy consumption are not sensitive to how people live and work in buildings, a gap occurs between the expected and actual performance of those technologies. This paper explores this problem using the concepts of 'design logic' (designers' ideas, values, intentions and user representations) and the 'user logic' (related in this case to how building occupants currently live and work in a building). The research presented unpacks the 'design logic' embedded in DR approaches planned for implementation at four blocks of buildings in a Horizon 2020 funded project, called "Demand Response in Blocks of Buildings" (DR-BoB). It discusses how the 'user logic' may differ from the 'design logic' and the potential impact of this on the performance of the technologies being implemented to reduce or shift energy consumption. The data analysed includes technical working documents describing the implementation scenarios of DR at four pilot sites, interviews and workshops conducted with the project team and building occupants during the first phases of the project. The analysis presented identifies how expectations about building occupants and their behaviours are built into the DR scenarios (to be tested during the project demonstrations). Initial findings suggest that building occupants' energy use practices and routines may be different from those expectations. The paper illustrates how the concepts of 'design logic' and 'user logic' can be used to identify mismatches before technologies are implemented. The paper concludes with recommendations for improving the design and implementation of DR. [ABSTRACT FROM AUTHOR]

This paper proposes a micro-simulation assessment of the distributional impacts of the French carbon tax. It shows that the policy is regressive, but could be made progressive by redistributing the revenue through flat-recycling. However, it would still generate large horizontal distributive effects and harm a significant share of low-income households. The determinants of the tax incidence are characterized precisely, and alternative targeted transfers are simulated on this basis. The paper shows that given the importance of unobserved heterogeneity in the determinants of energy consumption, horizontal distributive effects are much more difficult to tackle than vertical ones. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the determinants of decline in energy intensity in four Chinese industries—pulp and paper, cement, iron and steel, and aluminum. This paper attempts to answer the following key question: For the purpose of promoting energy efficiency, do prices, technology, enterprise restructuring and other policy-related instruments affect various sectors uniformly so as to justify uniform industrial energy conservation policies, or do different industries respond significantly differently so as to require policies that are tailored to each sector separately? In this paper, we examine this question using data for China's most energy-intensive large and medium-size enterprises over the period 1999-2004. Our results suggest that in all four industries rising energy costs are a significant contributor to the decline in energy intensity over our period of study. China's industrial policies encouraging consolidations and scale economies also seem to have contributed to reductions in energy intensity in these four industries. [ABSTRACT FROM AUTHOR]

Achieving a transition to a low-carbon energy system is now widely recognised as a key challenge facing humanity. To date, the vast majority of research addressing this challenge has been conducted within the disciplines of science, engineering and economics utilising quantitative and modelling techniques. However, there is growing awareness that meeting energy challenges requires fundamentally sociotechnical solutions and that the social sciences have an important role to play. This is an interdisciplinary challenge but, to date, there remain very few explorations of, or reflections on, interdisciplinary energy research in practice. This paper seeks to change that by reporting on an interdisciplinary experiment to build new models of energy demand on the basis of cutting-edge social science understandings. The process encouraged the social scientists to communicate their ideas more simply, whilst allowing engineers to think critically about the embedded assumptions in their models in relation to society and social change. To do this, the paper uses a particular set of theoretical approaches to energy use behaviour known collectively as social practice theory – and explores the potential of more quantitative forms of network analysis to provide a formal framework by means of which to diagram and visualise practices. The aim of this is to gain insight into the relationships between the elements of a practice, so increasing the ultimate understanding of how practices operate. Graphs of practice networks are populated based on new empirical data drawn from a survey of different types (or variants) of laundry practice. The resulting practice networks are analysed to reveal characteristics of elements and variants of practice, such as which elements could be considered core to the practice, or how elements between variants overlap, or can be shared. This promises insights into energy intensity, flexibility and the rootedness of practices (i.e. how entrenched/established they are) and so opens up new questions and possibilities for intervention. The novelty of this approach is that it allows practice data to be represented graphically using a quantitative format without being overly reductive. Its usefulness is that it is readily applied to large datasets, provides the capacity to interpret social practices in new ways and serves to open up potential links with energy modelling. More broadly, a significant dimension of novelty has been the interdisciplinary approach, radically different to that normally seen in energy research. This paper is relevant to a broad audience of social scientists and engineers interested in integrating social practices with energy engineering. [ABSTRACT FROM AUTHOR]

Confronted with the contemporary challenges of maximising energy efficiency with minimal impact on the environment, the automotive industry has developed various technologies to tackle them. Most of these technologies, however, have wider implications on the tribological performance of the automotive engines due to resultant soot build-up. This paper reviews the effects that attempts by stakeholders to satisfy requirements for reduced fuel consumption, reduced emissions and extended service intervals have had on increasing soot levels to an extent that can lead to engine component failure. Three areas have been identified that have either not been explored or not widely explored in the study of automotive soot namely: numerical simulation and modelling of soot wear, soot effects on wear of actual engine components and the wear and friction performance of non-metallic materials used in internal combustion engines. A paper-grading system is also utilised to present an overview of how sooty oil-related research covers various areas. [ABSTRACT FROM AUTHOR]

Imperfect information on future energy costs can lower the demand for more energy efficient technologies, with clear implications for energy supply, emissions and climate change. These hypotheses are explored using a randomised discrete choice experiment for property rental decisions. Results show that when energy consumption is expressed in physical units (as per the current EU labels) the willingness- to-pay for energy efficiency improvements is small and marginally significant. While bimonthly energy cost information displayed in monetary terms has no effect on the valuation of energy efficiency, annual monetary energy cost information led to a significant increase. This is the first paper to compare short and long-term point of sale energy cost information for household property decisions. There are clear implications for labelling policy for properties--framing energy consumption according to long-term monetary cost increases the demand for energy efficiency. [ABSTRACT FROM AUTHOR]

To meet the energy demand, over and above to build a clean pollution-free environment, wind energy is playing an incredible role. The development in the field of wind power generation is rising speedily. Operation and maintenance of the nonlinear wind energy conversion system (WECS) with stochastic wind input are pivoted on the control techniques used for the overall system. Many research works are undergoing which are based on various control schemes for different parts of WECS. Thus, this paper presents a review of advanced modern control strategies utilized in different parts of WECS with two popular types of grid-connected generators to achieve the desired power level and fault-free operation developed in the last 15 years. The review paper intends to provide a suitable reference for further research in the field of wind energy to have a bird's eye view from the tabulated information and presentation. [ABSTRACT FROM AUTHOR]

In recent years, Ethiopia has been developing different strategies to promote the installation of renewable energy plants. In this regard, evaluating territories to assess their potential and thus identify optimum sites for the installation of energy-generating facilities is a crucial task. This paper presents a comprehensive geographic information system (GIS)-based site-selection methodology for wind-power plants in the province of Amhara. High resolution wind speed data from Ethiopian National Metrology Agency (ENMA) and lower resolution daily wind speed data are combined and processed in order to obtain a fine wind speed distribution and thus wind energy production. This assessment is vital to elevate proper utilization of the energy sources into one step. According to the results we have obtained, in this paper, appreciated results are obtained to the power enhancement of the country. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBAN) is an emerging technology, a subset of Wireless Sensor Network. WBAN is a collection of pieces of tiny wireless body sensors with small computational capability and communicates short distance using ZigBee or Bluetooth. The main application of WBAN is in healthcare industry like remote patient monitoring. The small pieces of sensor monitor health factors like body temperature, pulse rate, ECG, heart rate etc., and communicate it to the base station or central coordinator for aggregation or for data computation. The final data is communicated to remote monitoring devices through internet or cloud service providers. The main challenge of this technology is dead nodes due to high energy consumption with all the wireless node working on battery. Minimization of the energy consumption extends life of the network. Security is another major challenge. There are possibilities of internal attacks being executed by malicious nodes, creating problems for the network. This paper proposes a model which provides solution for extending the life span of the network by minimizing energy consumption and also proposes model to provide solution for internal soft attacks created within the network through calculation or trust, computation among nodes to identify malicious nodes with the help of social-and QoS-based trust computation for secure clustering and communication. The proposed model is compared with LEACH and LEACH-MM protocol and performance is measured with various parameters. [ABSTRACT FROM AUTHOR]

Due to climate change conditions, natural ventilation potential may reduce over the years and increase dependence on HVAC systems. Moreover, occupants' behaviour regarding natural ventilation is a significant parameter affecting the thermal-energy performance of residential buildings as people tend to occupy their homes differently depending on their life, work and cultural routines. Therefore, in this study, the thermal-energy performance of a Global South (GS) housing case study located in Brazil was assessed in a future weather context. This paper included two major steps: (1) Optimization procedure to create optimized models based on different occupancy patterns; and (2) Parametric analysis to explore the building's thermal-energy performance for a given constructive design option, occupant behaviour and weather data. The optimization procedure included a multi-objective optimization based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to minimize discomfort hours and cooling energy demand, while parametric analysis explored the occupants' behaviour varieties derived from alternative occupancy patterns, ventilation availabilities and HVAC operation modes. The obtained future context simulation results indicated an increase in discomfort hours and cooling energy demand, while the most appropriate architecture design might vary depending on the occupancy behaviour. [ABSTRACT FROM AUTHOR]

With the recent dramatic growth of the building sector's energy consumption to provide thermal comfort to consumers, the constraints of climate change and the scarcity of energy supplies have necessitated a wiser and more effective reduction in the amount consumed. As a result, phase change materials (PCMs) have attracted much interest as latent heat thermal storage systems in recent decades. This paper aims to explain the possibility of using PCMs in buildings by establishing an overall conceptual framework for the processes of selecting and integrating PCMs in building sector applications. Moreover, it leads to a comprehensive matrix that identifies the best and most common type of material for each building's component, as well as its temperature and thickness properties, its thermal impact, and the most popular integration techniques. The findings show paraffin as the major used type in all building components, while salt hydrate is employed in facades. The most prevalent incorporating technique is macro-encapsulation, which prevails in most building components with its direct effect on cooling and heating applications. [ABSTRACT FROM AUTHOR]

Reducing energy consumption and Greenhouse Gas (GHG) emissions is an essential part of the clean growth and climate change framework recently developed by the Canadian government, which emphasizes the importance of energy-efficient building constructions. In this paper, the effects of thermal mass and placement of the thermally massive layer within wall assemblies on the transient thermal performance of walls and energy performance of a case study office building were studied. Three climate conditions representative of the heating-dominated, temperate, and cooling-dominated climates were considered. As for the assessment of energy demands, two cases for the indoor air temperature were taken into account: (i) indoor temperature was maintained at 20°C throughout the year, and (ii) during summertime, there was a set-point of 24°C and a setback of 35°C during the rest of the day while during wintertime, the set-point and setback values were 22°C and 18°C, respectively. The cases were compared according to the resulting decrement factor, the time required to reach quasi-steady state conditions, amplitudes of changes of heat fluxes and indoor surface temperatures, and the energy demands. The results showed that, for the cases studied, the wall, for which the thermally massive layer is not directly exposed to the indoor and outdoor climate conditions, resulted in the lowest decrement factor, the minimum amplitude of changes of heat fluxes and indoor surface temperatures, and maximum time required to reach quasi steady-state conditions. As for the energy performance, on the other hand, the wall, for which the thermally massive layer is exposed to the interior and exterior climate conditions, performed best amongst the cases investigated. [ABSTRACT FROM AUTHOR]

Unsteady behaviour of hydronic heating systems causes higher mean room temperatures than are required for comfort. Peak room temperatures depend on interactions between thermostats, heat emitters and the room. The importance of fluid properties on such unsteady heating is often misunderstood meaning potential energy savings are overlooked. This paper demonstrates the influence of fluid modifications and indicates a plausible magnitude of the energy saving opportunity. The results showed that fluid side heat transfer coefficient in isolation had negligible effect. Specific heat capacity of the fluid and flow rates were important, as they altered the amount of embedded energy in the heat emitter when thermostat conditions were met. Reductions in mean heating power for steady demand conditions were between 0 and 7% for plausible changes to fluid properties, depending on heat emitter size, room insulation and external temperature. Reductions in individual cycle energy were between 5 and 25%. When considered in the context of intermittent finite duration heating events, those that contained a small number of thermostat cycles demonstrated energy savings that tended towards the reductions in individual cycle energy. Heating events with larger numbers of cycles showed energy savings tending towards the reduction in mean heating power. [ABSTRACT FROM AUTHOR]

This paper assesses the nexus between natural resource rent, hydropower energy consumption and ecological footprint for environmental sustainability incorporating with economic performance by using yearly data from 1970 to 2017. To this end, the long run steady-state relationship among investigated variables has been analyzed by using newly established Residual Least Squares (RALS) method and confirmed for Turkish economy. The directions and magnitudes of the effects of these variables on ecological footprint were tested by using Dynamic Ordinary Least Squares (DOLS) technique. The findings, except hydropower energy consumption, reveal statistically significant and positive effects of investigated variables on ecological footprint. Moreover, the inverted U-shaped EKC hypothesis was also affirmed between economic performance and ecological footprint. The causal relationship among these variables were also tested by using VECM Granger causality test The unidirectional Granger causality relationships were found that they are running from ecological footprint, and natural resource rent to economic performance, from natural resources to ecological footprint and from natural resource rent to economic performance in Turkish economy. Additionally, the bidirectional causal relationship has been detected among economic performance and ecological footprint in Turkey. The findings highlight the importance of investigated variables on ecological footprint in Turkey. [ABSTRACT FROM AUTHOR]

To figure out at what extent of China's investment in energy technology in recent years has helped economic growth and improved the environment. From the basic theory of environmental regulation's influence on energy consumption efficiency, this study uses the super-efficiency slack base model to measure the green energy efficiency of 30 provinces in China from 2010 to 2019, and uses the kernel density function to analyze evolution trajectory. Then, the spatial Dubin model is used to test the influence of some important economic variables. The results of value show that although China's green energy efficiency improved significantly from 2010 to 2019, the overall performance was lacking. However, the overall efficiency value was significantly improved during the study period. Moreover, the efficiency value was closely related to the performance of economic development. From the dynamic trajectory perspective, efficiency values of all years show the type of "two peaks".Altough the width between the two peaks did not change noticeably, but the efficiency still had a significantly improved. In addition, according to the results of spatial economic regression analysis, eco-environmental constraints, foreign direct investment, and industrial agglomeration were not conducive to the efficiency value, while industrial structure and manufacturing enterprise scale had positive effects. In addition, the influence effects had obvious spatial phenomena. Therefore, for the Chinese government, enterprises and society, the next step is not only to vigorously exploit energy-saving and emission reduction technologies to improve energy consumption efficiency but also to establish reasonable environmental regulation policies to push enterprises to produce in a cleaner way. Additionally, a reasonable regional development plan should be established to promote the rational distribution and flow of the energy industry across different places. This paper provides a novel perspective for the study of energy input and usage. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate bi-directional spillovers into residential and industrial sectors induced by energy efficiency improvement (EEI) in both the short- and long-term, and the impact of nesting structure as well as the size of elasticities of substitution of production and utility functions on the magnitude and the transitional dynamic of rebound effect. Developing a dynamic general equilibrium model, we demonstrate that residential EEIs spillover into the industrial sector through the labor supply channel and industrial EEIs spillover into the residential sector through the conventional income channel. Numerical simulations calibrated on the U.S. suggest that not taking into account these spillover effects could lead to misestimating the rebound effect notably of residential sector EEIs. We also demonstrate how the size and the duration of the rebound effect depend on the elasticities of substitution’s values. Numerical simulations suggest that alternative sets of value for the elasticities of substitution may give different sizable patterns of rebound effects in both the short- and long-term. In policy terms, our results support the idea that energy efficiency policies should be implemented simultaneously with rebound effect offsetting policies by considering short- and long-term economy feedbacks. As a consequence, they require considering debates about what type of policy pathways are more effective in mitigating the rebound effect. [ABSTRACT FROM AUTHOR]

Evolutionary Algorithms (EAs) are routinely applied to solve a large set of optimization problems. Traditionally, their performance in solving those problems is analyzed using the fitness quality and computing time, and the effect of evolutionary operators on both metrics is routinely used to compare different versions of EAs. Nevertheless, scientists face nowadays the challenge of considering the energy efficiency in addition to computational time, which requires studying the energy consumption of algorithms. This paper discusses the interest of introducing power consumption as a new metric to analyze the performance of standard genetic programming (GP). Two well-studied benchmark problems are addressed on three different computing platforms, and two different approaches to measure the power consumption have been tested. Analyzing the population size, the results demonstrates its influence on the energy consumed: a non-linear relationship was found between size and energy required to complete an experiment. This analysis was extended to the cache memory and results show an exponential growth in the number of cache misses as the population size increases, which affects the energy consumed. This study shows that not only computing time or solution quality must be analyzed, but also the energy required to find a solution. Summarizing, this paper shows that when GP is applied, specific considerations on how to select parameter values must be taken into account if the goal is to obtain solutions while searching for energy efficiency. Although the study has been performed using GP, we foresee that it could be similarly extended to EAs. [ABSTRACT FROM AUTHOR]

Carbon dioxide is the largest component of the human footprint and one of the major components of all greenhouse gases. The expected increase in population will lead to growth in energy consumption and greenhouse gas emissions. The building industry has the highest potential for reducing greenhouse gas emissions. Therefore, buildings should become not only efficient consumers but also energy producers, not a simple task in dense cities. The paper describes the feasibility and limitations of near zero energy design in highly dense urban conditions. The study was carried out by examination and comparison of various density design, alternatives of an existing urban plot in the coastal climate zone of Israel. Increased dwelling units' number leads to higher total energy use on the one hand and mutual shading of new high-rise residential buildings on the other. Preserving solar rights for PV systems installation become more complex. The relation between urban density and solar rights in urban design, energy consumption and energy generation within plot borders and their implications are presented and discussed in the paper. [ABSTRACT FROM AUTHOR]

The air pollution situation in the northern part of China has become a threat to the current status of China's energy consumption. This has created pressure to relocate coal plants to the western section of the country. We apply a spatial panel data method to empirically examine the relationship between fine particulate matter (PM2.5) concentration, precipitation and wind speed by using the sample data of 50 cities over 11 months. Based on the empirical parameters, the paper simulates the potential impacts of the proposed coal plants and the coal plants currently under construction for the PM2.5 concentrations across all cities once these coal plants are operational. The results show that the new capacities will increase PM2.5 concentrations in northwest and northeast China, but at acceptable levels and lower than levels in Beijing, Tianjin, Hebei and Shandong. [ABSTRACT FROM AUTHOR]

The aggravation of the greenhouse gas emission-resulted climate change poses a significant challenge to the world. In response to the urgent need for significantly more sustainable societies, this paper develops a protocol for the optimal design of residential balcony based on environmental benefits in the case study of China's hot summer and cold winter area. The study utilized ROI and AY as environmental performance indicators for decision-making scheme investment and EnergyPlus to simulate the A/C energy-saving potential of considered balcony design schemes. The literature and experiment validation results show that EnergyPlus with Shadow Algorithm is reliable for studying the energy consumption of residence with balcony. The added embodied environmental impact of the different design schemes was estimated, and the idea of life cycle assessment was used for quantitative evaluation of the carbon emissions of the balcony. The results show the depth of 1.2 m suits the south/north-facing balcony, and a big window-wall ratio of 0.6 was the most beneficial in the environmental performance of the balcony investment. When the balcony is attached to different rooms, the environmental profitability is influenced by the factors such as occupancy behaviour and balcony environmental investment. The attached room type of residence on a case-by-case basis should be analyzed. [ABSTRACT FROM AUTHOR]

Global climate change has caused governments worldwide to take actions to improve their energy efficiency. This paper investigates how China's Top-1000 program, a command-and-control type of energy-saving mandate, has affected the operational choices of firms, and in turn, their profitability. We apply the propensity score matching method to find "identical twins" for the participants in the Top- 1000 program, then conduct a difference-in-differences analysis on the matched sample. Our findings suggest that the profitability of the enterprises targeted for energy savings decreased by one-third, mainly due to increased production costs. The targeted enterprises tended to increase their fixed assets per capita, which was associated with improvements in energy efficiency. Furthermore, compared to similar untargeted enterprises, there was a significant slowdown in the production growths of the targeted enterprises, raising concerns about carbon leakage due to increased production by less efficient producers. [ABSTRACT FROM AUTHOR]

Following the European Union's implementation of Energy Performance Certificates (EPCs) for buildings, the capitalization of energy efficiency in transaction prices and rents has been subject to much research. This paper uses different identification strategies for the Norwegian residential sales (N = 750,000) and rental (N = 670,000) markets to highlight the endogeneity and methodological limitations associated with assessing the price effects of energy efficiency and the signaling effect of label adoption. We find that the valuation of energy efficiency is subject to unobserved location and quality bias, that labeling has immediate, short-run, and long-run price effects and that different effects are observed in different submarkets. We provide evidence that sample selection issues related to location and time, with methodological and data limitations, are essential factors that must be considered when assessing the effects of the EPC implementation. [ABSTRACT FROM AUTHOR]