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The energy rebound effect is a potential threat to energy-saving targets based on energy efficiency improvements. This paper employs a stochastic energy demand frontier model to analyze the energy demand and rebound effect in China’s urban residential sector. Using a panel data set of 30 Chinese provincial-level regions over the period 2001–2014, for the first time, we investigate the degrees and determinants of China’s urban residential energy demand and energy rebound effect. The results show that residents’ income level, energy price, temperature deviation, population scale, household size, and district heating system are significant influencing factors of residential energy consumption. Regarding the energy rebound effect, we find that energy price is negatively correlated with the rebound effect, and an inverted U-shaped relationship between residents’ income level and rebound-effect size exists. The magnitude of the rebound effect varies across regions, with an average of 65.4%. The main policy implication generated by this study is that it should be in urgent need of energy pricing reform to mitigate the rebound effect in China.

PURPOSE: Environmental life cycle assessment (LCA) is today an important methodology to quantify the life cycle based environmental impacts of products, services or organisations. Since the very first LCA studies, the cumulative energy demand CED (also called ‘primary energy consumption’) has been one of the key indicators being addressed. Despite its popularity, there is no harmonised approach yet and the standards and guidelines define the cumulative energy demand differently. In this paper, an overview of existing and applied life cycle based energy indicators and a unifying approach to establish characterisation factors for the cumulative energy demand indicator are provided. The CED approaches are illustrated in a building’s LCA case study. METHODS: The five approaches are classified into two main concepts, namely the energy harvested and the energy harvestable concepts. The two concepts differ by the conversion efficiency of the energy collecting facility. A unifying ‘energy harvested’ approach is proposed based on four theses, which ensure consistent accounting among renewable and non renewable energy resources. RESULTS AND DISCUSSION: The indicator proposed is compared to four other CED indicators, differing in the characterisation factors of fossil and biomass resources (upper or lower heating value), the characterisation factor of uranium and the characterisation factors of renewable energy resources (amount harvested or amount harvestable). The comparison of the five approaches is based on the cumulative energy demand of a newly constructed building of the city of Zürich covering the whole life cycle, including manufacturing and construction, replacement and use phase, and end of life. The cumulative energy demand of the life cycle of the building differs between 336 MJ oil-eq/m²a (‘CED uranium low’) and 836 MJ oil-eq/m²a (‘CED energy statistics’). The main differences occur in the use phase. The main reason for the large differences in the results are the different concepts to determine the characterisation factors for renewable and nuclear energy resources. CONCLUSIONS: The energy harvested approach ‘CED standard’ is a consistent approach, which quantifies the energy content of all different (renewable and non-renewable) energy resources. The ‘CED standard’ approach and the impact category indicator results computed with this approach reflect the safeguard subject ‘energy resources’ but not (no other) environmental impacts. The energy harvested approach proposed in this paper can readily be implemented in different contexts and applied to various data sets.

In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

As household incomes rise around the world and global temperatures go up, the use of air conditioning is poised to increase dramatically. Air conditioning growth is expected to be particularly strong in middle-income countries, but direct empirical evidence is scarce. In this paper we use high-quality microdata from Mexico to describe the relationship between temperature, income, and air conditioning. We describe both how electricity consumption increases with temperature given current levels of air conditioning, and how climate and income drive air conditioning adoption decisions. We then combine these estimates with predicted end-of-century temperature changes to forecast future energy consumption. Under conservative assumptions about household income, our model predicts near-universal saturation of air conditioning in all warm areas within just a few decades. Temperature increases contribute to this surge in adoption, but income growth by itself explains most of the increase. What this will mean for electricity consumption and carbon dioxide emissions depends on the pace of technological change. Continued advances in energy efficiency or the development of new cooling technologies could reduce the energy consumption impacts. Similarly, growth in low-carbon electricity generation could mitigate the increases in carbon dioxide emissions. However, the paper illustrates the enormous potential impacts in this sector, highlighting the importance of future research on adaptation and underscoring the urgent need for global action on climate change. Significance The use of air conditioning is poised to increase dramatically over the next several decades as global temperatures go up and incomes rise around the world. In this paper, we use high-quality microdata from Mexico to characterize empirically the relationship between temperature, income, and air conditioning. We describe both how electricity consumption increases with temperature given current levels of air conditioning, and how climate and income drive air conditioning adoption decisions. We then combine these estimates with predicted end-of-century temperature changes to forecast future energy consumption. Overall, our results point to air conditioning impacts being considerably larger than previously believed.

Over the next 25 to 30 years, nearly all of the growth in energy demand, fossil fuel use, associated local pollution, and greenhouse gas emissions is forecast to come from the developing world. This paper argues that the world's poor and near-poor will play a major role in driving medium-run growth in energy consumption. As the world economy expands and poor households' incomes rise, they are likely to get connected to the electricity grid, gain access to good roads, and purchase energy-using assets like appliances and vehicles for the first time. We argue that the current forecasts for energy demand in the developing world may be understated because they do not accurately capture growth in demand along the extensive margin, as low-income households buy their first durable appliances and vehicles. Within a country, the adoption of energy-using assets typically follows an S-shaped pattern: among the very poor, we see little increase in the number of households owning refrigerators, vehicles, air conditioners, and other assets as incomes go up; above a first threshold income level, we see rapid increases of ownership with income; and above a second threshold, increases in ownership level off. A large share of the world's population has yet to go through the first transition, suggesting there is likely to be a large increase in the demand for energy in the coming years.

The residential energy demand is growing steadily and the trend is expected to continue in the near future. At the same time, under the impulse of economic crises and environmental and energy policies, many households have experienced reductions in real income and higher energy prices. In the residential sector, the number of fuel-poor households is thus expected to rise. A better understanding of the determinants of residential energy demand, in particular of the role of income and the sensitivity of households to changes in energy prices, is crucial in the context of recurrent debates on energy efficiency and fuel poverty. We propose a panel threshold regression (PTR) model to empirically test the sensitivity of French households to energy price fluctuations—as measured by the elasticity of residential heating energy prices—and to analyze the overlap between their income and fuel poverty profiles. The PTR model allows to test for the non-linear effect of income on the reactions of households to fluctuations in energy prices. Thus, it can identify specific regimes differing by their level of estimated price elasticities. Each regime represents an elasticity-homogeneous group of households. The number of these regimes is determined based on an endogenously PTR-fixed income threshold. Thereafter, we analyze the composition of the regimes (i.e. groups) to locate the dominant proportion of fuel-poor households and analyse their monetary poverty characteristics. Results show that, depending on the income level, we can identify two groups of households that react differently to residential energy price fluctuations and that fuel-poor households belong mostly to the group of households with the highest elasticity. By extension, results also show that income poverty does not necessarily mean fuel poverty. In terms of public policy, we suggest focusing on income heterogeneity by considering different groups of households separately when defining energy efficiency measures. We also suggest paying particular attention to targeting fuel-poor households by examining the overlap between fuel and income poverty.

Owing to the high energy demand of buildings, which accounted for 36% of the global share in 2020, they are one of the core targets for energy-efficiency research and regulations. Hence, coupled with the increasing complexity of decentralized power grids and high renewable energy penetration, the inception of smart buildings is becoming increasingly urgent. Data-driven building energy management systems (BEMS) based on deep reinforcement learning (DRL) have attracted significant research interest, particularly in recent years, primarily owing to their ability to overcome many of the challenges faced by conventional control methods related to real-time building modelling, multi-objective optimization, and the generalization of BEMS for efficient wide deployment. A PRISMA-based systematic assessment of a large database of 470 papers was conducted to review recent advancements in DRL-based BEMS for different building types, their research directions, and knowledge gaps. Five building types were identified: residential, offices, educational, data centres, and other commercial buildings. Their comparative analysis was conducted based on the types of appliances and systems controlled by the BEMS, renewable energy integration, DR, and unique system objectives other than energy, such as cost, and comfort. Moreover, it is worth considering that only approximately 11% of the recent research considers real system implementations.

Human food systems are a key contributor to climate change and other environmental concerns. While the environmental impacts of diets have been evaluated at the aggregate level, few studies, and none for the US, have focused on individual self-selected diets. Such work is essential for estimating a distribution of impacts, which, in turn, is key to recommending policies for driving consumer demand towards lower environmental impacts. To estimate the impact of US dietary choices on greenhouse gas emissions (GHGE) and energy demand, we built a food impacts database from an exhaustive review of food life cycle assessment (LCA) studies and linked it to over 6000 as-consumed foods and dishes from 1 day dietary recall data on adults (N = 16 800) in the nationally representative 2005-2010 National Health and Nutrition Examination Survey. Food production impacts of US self-selected diets averaged 4.7 kg CO2 eq. person−1 day−1 (95% CI: 4.6-4.8) and 25.2 MJ non-renewable energy demand person−1 day−1 (95% CI: 24.6-25.8). As has been observed previously, meats and dairy contribute the most to GHGE and energy demand of US diets; however, beverages also emerge in this study as a notable contributor. Although linking impacts to diets required the use of many substitutions for foods with no available LCA studies, such proxy substitutions accounted for only 3% of diet-level GHGE. Variability across LCA studies introduced a ±19% range on the mean diet GHGE, but much of this variability is expected to be due to differences in food production locations and practices that can not currently be traced to individual dietary choices. When ranked by GHGE, diets from the top quintile accounted for 7.9 times the GHGE as those from the bottom quintile of diets. Our analyses highlight the importance of utilizing individual dietary behaviors rather than just population means when considering diet shift scenarios.

Firms and governments often use moral suasion and economic incentives to influence intrinsic and extrinsic motivations for economic activities. To investigate persistence of such interventions, we randomly assign households to moral suasion and dynamic pricing that stimulate energy conservation during peak-demand hours. We find significant habituation and dishabituation for moral suasion—the treatment effect diminishes after repeated interventions but can be restored to the original level by a sufficient time interval between interventions. Economic incentives induce larger treatment effects, little habituation, and significant habit formation. Our results suggest moral suasion and economic incentives produce substantially different short-run and long-run policy impacts.

Large fluctuations in energy prices have been a distinguishing characteristic of the U.S. economy since the 1970s. Turmoil in the Middle East, rising energy prices in the United States, and evidence of global warming recently have reignited interest in the link between energy prices and economic performance. This paper addresses a number of the key issues in this debate: What are energy price shocks and where do they come from? How responsive is energy demand to changes in energy prices? How do consumer's expenditure patterns evolve in response to energy price shocks? How do energy price shocks affect U.S. real output, inflation, and stock prices? Why do energy price increases seem to cause recessions but energy price decreases do not seem to cause expansions? Why has there been a surge in the price of oil in recent years? Why has this new energy price shock not caused a recession so far? Have the effects of energy price shocks waned since the 1980s and, if so, why? As the paper demonstrates, it is critical to account for the endogeneity of energy prices and to differentiate between the effects of demand and supply shocks in energy markets when answering these questions.