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• analyze the energy efficiency of Beijing's industrial sectors from 2002 to 2018. • model industry heterogeneity of Beijing's industrial sectors accurately. • low energy efficiency of Beijing's industrial sectors due to management inefficiency. • the energy efficiency of heavy industry is higher than that of light industry. • explore the influencing factors of energy efficiency of Beijing's industrial sectors. Improving energy efficiency is one of the effective means to achieve China's carbon peaking and carbon neutrality goals. As the capital of China, Beijing should improve energy efficiency and has a primary responsibility in achieving the goals. Considering the heterogeneity of industrial sectors, this paper proposes a data envelopment analysis (DEA) approach to analyze the total factor energy efficiency and its influencing factors of 31 industrial sectors in Beijing from 2002 to 2018. We show that the low energy efficiency of Beijing's industrial sectors, mainly due to management inefficiency, is not conducive to the realization of the goals. Besides, the energy efficiency of heavy industry is higher than that of light industry. The expansion of enterprise scale has a positive impact on energy efficiency, while the property right structure, foreign trade and energy consumption structure have a negative impact on energy efficiency. Finally, we put forward some policy recommendations. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [ABSTRACT FROM AUTHOR]

• Long-term operational monitoring of a Zero Emission Building (ZEB) in Beijing. • An existing building being retrofitted into a Zero Emission Building (ZEB). • Key Building Mounted Photovoltaic (BMPV) technologies are practically utilized and their effects are estimated. • The ZEB realized its own net-zero carbon emission and net energy production. • The ZEB could be a comprehensive experimental platform. Building mounted photovoltaic (BMPV) technology is a promised way to achieve carbon peak in building sector. Focused on the influence of heat from PV modules on building envelope's heat transfer and energy saving performance, and the power generation, the coupling mechanism of power generation and heat transfer of integrated photovoltaic technology on building façades has been investigated. The synergy of solar energy generation and building energy consumption have been studied in this paper. The installation method of building PV system was proposed, with which can achieve the maximum energy saving. Furthermore, the achievements have been applied in Zero Emission Building (ZEB) of China Academy of Building Research (CABR), demonstrating way to realize zero carbon building by integration of photovoltaic technology. Total of 236.1 kWp PV systems were installed in ZEB. Monitoring data shows that the annual power generation of ZEB could reach 212,382 kWh, and the power generation per building area is 73.79 kWh/m2, which could meet all the energy demands. In addition, it can supply extra power to surrounding buildings. The ZEB can provide experience and practice data supporting carbon peak in building sector. [ABSTRACT FROM AUTHOR]

For traditional air cooling/heating process in HVAC system, only single temperature of water generated by mechanical equipment is used to handle air cooling/heating load. However, there is the potential to simultaneously use waters at different temperatures to finish air cooling/heating process, which has shown a significant energy-saving effect in previous studies. Currently, there is no theoretical method for efficiently guiding the simultaneous use of multiple energies, especially natural/waste energies, for handling air cooling/heating load. Thus, this paper proposes a construction method for air cooling/heating process based on grade match between energy and load (GMEL). Considering the air cooling/heating process of typical electronic factory in Beijing as example, new air cooling/heating systems are constructed according to load grade and local natural/waste energies. Furthermore, the energy use is compared with that of traditional air cooling/heating system. The results reveal the following: (1) The GMEL method can construct air cooling/heating process that fully uses natural/waste energies to directly handle air load as much as possible, applies water at different temperatures generated by mechanical equipment to handle the corresponding grade of load, and realizes mutual utilization of air cooling and heating loads. (2) The energy-savings rate of the constructed air cooling/heating system of typical electronic factory in summer/winter is 26.7%/52.4%. The static payback period is 0.83 year. (3) The specialized air cooling/heating processes are different for different local natural/waste energies and economic condition. The method will promote the full usage of renewable energy worldwide, and significantly improve the energy efficiency of mechanical cooling/heating source. [ABSTRACT FROM AUTHOR]

The "Reverse power flow" (RPF) behavior of distributed photovoltaics (DPV) promotes the new energy consumption and has a negative impact on the adaptability of the distribution network (DN). Researching and constructing a DN adaptability assessment model considering the DPV's RPF behavior is an important method to explore the weaknesses of DN adaptability from the perspective of management decision-making, and then propose improvement solutions. Firstly, this paper constructs a set of hierarchical DN adaptability assessment index systems from the three dimensions of security, economy and low carbon. Secondly, the combination of ordered weighted averaging improved the Analytic Hierarchy Process and the Entropy weight method is used to weigh the indicators. Then, an assessment method based on the combination of credibility theory and cloud model is proposed to solve the ambiguity and randomness in the evaluation process. Finally, the model was applied practically in the DN of Qiantuan Village in Beijing, and the adaptation of the DN is compared and analyzed under three scenarios: DPV not grid-connected, grid-connected and fully local consumption, grid-connected and RPF behavior. The results show that the adaptability levels of Qiantan Village DN are classified as "Medium", "Good", and "Good", with scores of 78.839, 86.312, and 88.287, respectively, under the three different scenarios. Furthermore, regarding the first-level indicators, the DPV's RPF behavior reduces the security of the DN but improves the adaptability in general. This paper's research can accurately locate the weak points based on reflecting the level of DN adaptability, thereby providing a reference for the formulation of a DN adaptability improvement plan and the construction of a clean and low-carbon DN. • Constructed a set of distribution network adaptability assessment index systems. • Propose a Combined weighting method by combining Ordered Weighted Averaging improved AHP and the Entropy weight method. • Established a distribution network adaptability assessment model based on the credibility theory and cloud model. • The adaptability evaluation model is applied to the distribution network of Qiantuan village in Beijing. [ABSTRACT FROM AUTHOR]

• A physics-informed data-driven model is presented for fuel consumption and battery degradation. • The commuting-oriented hybrid powertrain optimization with multiple objectives is carried out. • The commuting scenario is synthesized by a method combining Markov chain and neural network. • A novel hybrid powertrain with multiple drive modes is modeled based on vehicle bench test data. • The optimized solution can improve the vehicle performance under the commuting driving cycle. The physics-informed data-driven approach is instructive in powertrain modeling with high-fidelity, which can be adopted for powertrain optimization to further improve vehicle performances. In this paper, a plug-in hybrid electric vehicle (PHEV) equipped with a novel powertrain is considered as the research object, and a series of vehicle bench experiments are carried out for operative data acquisition. With the collected data, the physics-informed data-driven models for engine fuel consumption and battery state of health (SOH) loss are proposed. The accuracy and superiority of the proposed model are demonstrated compared with other approaches. In order to realize the commuting-oriented powertrain optimization, the driving cycle representing the commuting scenario is reconstructed based on the collected traffic data in Beijing. The synthesized driving cycle generated by the proposed method can describe the practical application scenario in Beijing within the allowable error range. In addition, the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to further optimize the parameters for the hybrid powertrain according to vehicle performances. The multi-objective optimization is completed with the engine fuel consumption and battery state of health (SOH) loss as targets. The results indicate that the optimized configuration parameters can reduce fuel consumption by 4.85% and battery capacity loss by 3.7% compared with the original vehicle. [ABSTRACT FROM AUTHOR]

The scale-up of urban electric vehicle (EV) fleets, driven by environmental benefits, is resulting in surging aggregate energy demands that may reshape a city's power supply. This paper establishes an integrated data-driven assessment model for investigating the energy use (kWh) patterns and charging load (kW) profiles of urban-scale EV fleets. To this end, urban EV operating and operational datasets are linked with climate data and vehicle specifications. Four vehicle fleet types are distinguished: private, taxi, rental, and business fleets. Statistical models regarding distribution analysis, spectrum analysis, and identical distribution tests are employed to analyze the patterns of driving distances, energy consumption, and shares of active charging EVs. The minute-level changes in charging EV numbers and aggregate charging power are examined to reflect the grid load impact. The results show that private light-duty EVs in Beijing consume an average of 9.1 kWh/day, with more charging activities on Fridays. The primary load peaks of light-duty EVs in Beijing usually occur between 11 p.m. and 1 a.m., attributable chiefly to the private fleet's midnight peak load estimated at 28 % of the total daily charging private EV count multiplied by 5.5 kW/EV. Secondary peaks occur between 8 a.m. and 10 a.m. on weekdays for private fleets, and at 4 p.m. for public fleets. Our work can be extensively used for analyses on transport emissions, urban power supply, infrastructure build-ups, and policymaking. • An integrated data-driven model for large-scale EV operation analysis is proposed. • Real-world energy use patterns and load profiles of urban EV fleets are unveiled. • Urban-scale EV big data are utilized for fact-based assessments. • Diverse data sources are linked through spatiotemporal information. [ABSTRACT FROM AUTHOR]

Solar spectral irradiance distributions have a great impact on the photoelectric conversion performance of photovoltaic materials. By average photon energy, this paper assessed the practical conversion performance of ten types of photovoltaic materials based on the spectral measurements of Beijing and Changsha, China. Photon energy utilization efficiency was proposed to assess the practical conversion performance of photovoltaic materials at the same aperture area. Monocrystalline silicon had the best energy utilization efficiency when the spectrum is the red-rich or close to the reference spectrum. However, gallium arsenide would outperform it if the average photon energy exceeded 1.95 eV. Mismatch factor was used at the same rated output power of photovoltaic materials. It is found that the mismatch factor of perovskite with a 1.22 eV bandgap energy ranks the first place when the spectrum is the red-rich or close to the reference spectrum. However, the perovskite with a 1.83 eV bandgap energy has the optimal conversion performance at the average photon energy over 1.83 eV. Moreover, an ideal photovoltaic material with advanced properties was proposed for optimal performance with the regional spectra, which might provide useful instruction for photovoltaic industries to produce the most suitable photovoltaic material considering the annual regional spectrum. [ABSTRACT FROM AUTHOR]

Split-pane electrochromic (EC) windows enable the control of glare and the effective utilization of sunlight by adjusting the color of individual window panes. However, existing EC control methods are often limited to specific cases or require extensive simulation work, resulting in a deficiency of generic control approaches. To solve this issue, this paper proposed a novel approach to EC window zoning control based on glare prediction. This method evaluated real-time discomfort glare for each EC window pane and effectively adjusted the tinting state of the split panes to mitigate glare. A typical office setting in Beijing was selected to simulate the impact of utilizing this method on the light environment and energy conservation. The simulation results demonstrate that this approach could effectively keep the discomfort glare probability (DGP) below 0.4, which belongs to perceptible glare. Compared to EC whole window control, it reduced the tinting area multiplier hours by 63.64%–71.43%, managed UDI values within the range of 40%–90%, and reduced lighting energy consumption by over 40.98%. The results confirmed the accuracy of the proposed method in glare control, the improvement of the lighting environment, and the significant reduction in lighting energy consumption, enhancing its substantial energy-saving potential. [ABSTRACT FROM AUTHOR]

A heat pump driven atmospheric water harvesting system, which can be operated under direct cooling mode and desiccant wheels assisted cooling mode, was proposed in this paper. Investigations were carried out to find out the optimal operation mode and operation parameters under different ambient conditions and daily water harvesting requirements. Optimization studies were firstly carried out on each operation mode to find the recommended operation parameters, namely evaporation temperature and daily operation hours, for different ambient conditions, daily water harvesting rate and maximum operating hours. Then, the optimal operation modes, which has lower daily power consumption under their own recommended operation parameters, were suggested on the psychrometric chart. The results showed that, for different combinations of daily water harvesting rate and maximum operating hours, there exist a critical humidity ratio and a critical relative humidity ratio for each case. When ambient air relative humidity and humidity are both higher than the critical values, it is recommended to use the direct cooling mode. Otherwise, the desiccant wheels assisted cooling mode should be used. Lastly, performances of the system in different climate zones were analyzed. Under the optimal operation mode, the annual average water harvesting efficiency was from 1.08 to 1.22 kg/kWh in Urumqi, from 1.50 to 1.78 kg/kWh in Beijing, and from 2.22 to 3.72 kg/kWh in Haikou, respectively, when daily water harvesting rate is from 50 to 200 kg/day and maximum operating hours is from 6 to 10 h. • Propose a water harvesting system that can be operated under two operating modes. • Study the optimized operating parameters and performances for each operating mode. • Suggest optimal mode based on ambient conditions and water harvesting requirements. • Study annual performances in three typical cities with different climate conditions. [ABSTRACT FROM AUTHOR]

Integrated energy system (IES) is characterized by high self-consumption ratio of on-site generated renewable energy, high efficiency of conventional energy utilization and possesses a significant flexibility in its operation. This overall, constitute to the foundation of low-carbon communities. Considering economic and environmental benefits, this paper proposes a two-layer co-optimization model with the upper layer optimizing the IES configuration and the lower layer optimizing IES operation. A community in Beijing is introduced as a case study to analyze the benefits of IES and compared with the conventional energy system. Multiple scenarios are researched, including (a) IES for low-carbon communities with PV roof area limitation, (b) IES for each of the four building types with PV roof area limitation, (c) IES for low-carbon communities without PV roof area limitation. The results indicate that the optimized IES for community can reduce the cost by 25% and CO 2 emissions by 32% per year by considering the limitation of roof area for the PV system. Without this constraint, the costs and emissions could be reduced by 20% and 62%, respectively. In addition, IES is more appropriate for office and commercial buildings since their load characteristics allow for load shifting and community-level IES costs and emissions are 8% and 10% lower than building-level due to the complementary of community load. This study provides suggestions for the IES planning and application in low-carbon community. [Display omitted] • An IES with multiple renewable energy and energy storage subsystems is proposed. • A two-layer co-optimization model of IES design and operation is developed. • IES optimization results for building-level and community-level are compared. • Impact of roof area limitation for PV on IES optimization results is investigated. [ABSTRACT FROM AUTHOR]

• A reliable path finding model is proposed to minimize the fuel consumption budget. • Specified on-time arrival probability index is ensured by reliable path finding model. • A heuristic label setting algorithm is developed to exactly solve the formulated problem. • Real traffic data is applied for the applicability and efficiency verification. Transport activities are regarded as a major source of fuel consumption and CO 2 emission production. To reduce the negative impact of traffic-related CO 2 emission, this paper proposes a reliable path-finding algorithm for improving fuel efficiency in stochastic networks under the uncertainty of travel time and fuel consumption with the consideration of spatial correlation. A reliable constrained path-finding model is developed and formulated to minimize the fuel consumption budget while guaranteeing the specified on-time arrival probability. A heuristic label setting algorithm is developed to precisely solve the formulated problem. The proposed algorithm overcomes the time-consuming drawbacks of traditional path enumeration algorithms. The applicability and efficiency of the proposed algorithm are verified on real-world traffic data acquired from the Beijing and Xi'an networks in China. The experiments demonstrate that our proposed algorithm can significantly reduce fuel consumption compared to existing studies. The experiment in Beijing shows that using the proposed algorithm can reduce 0.9 kg of CO 2 emissions on average per trip compared to existing studies. [ABSTRACT FROM AUTHOR]

Promoting the coupled development of water, energy, and food (WEF) subsystems is a critical step to enhance synergies and increase efficiencies in the WEF nexus. However, the evolution and obstacles of coupled development are largely ignored. This study developed a framework dividing links in the nexus to select indicators and integrated models for the degree of coordination and obstacle diagnosis to explore the coupled development of the WEF nexus from 2000 to 2020 in the water-scarce megacities of Beijing and Tianjin. The results show that the average coordination degree of the WEF nexus in Beijing (0.315) and Tianjin (0.317) is at a low level, indicating a limited interaction between the WEF subsystems, while the coordinated development degree of the WEF nexus is increasing. The order degree of the WEF subsystems shows a two-level hierarchical structure, indicating that both similarities and differences are included in place-specific characteristics. Furthermore, obstacle factors are identified from water and energy subsystems, in which environmental water use (W3) and the energy consumption per gross domestic production unit (E4) played the most significant roles and require a higher priority in policy response. The results in this paper complement obstacle factor analysis in WEF nexus practice, and provide operational indicators for nexus governance. [Display omitted] • Linkages are divided into one-, two-, and three-resource scales. • The number of related official documents is used to define the weight of subsystems. • The water subsystem dominated the coupled development of the water-energy-food nexus. • The order degree of subsystems show a two-level hierarchical structure. • Key obstacle factors are identified from water and energy subsystems. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is one of the most effective ways to reduce energy waste and achieve carbon neutrality, but it may also bring the energy rebound effect and thus fail to achieve the desired energy conservations. In this paper, we examine the energy rebound effect of Beijing at the level of overall economy, industry and sector based on the Cobb-Douglas production function and the idea of Solow residual, so as to clarify the true impact of technological progress on energy-saving target. Results suggest that: (1) The overall economy of Beijing shows partial rebound effect in most years and appears backfire effect in some years, but the degree of the rebound effect has been gradually mitigated; (2) The energy rebound degree of the tertiary industry is higher than that of the secondary industry on the whole, but the rebound effect of the secondary industry fluctuates more obviously; (3) Energy savings at the sector level is also not ideal, and in most cases, sectors with low energy efficiency have a higher rebound effect than sectors with high energy efficiency. Based on these results, suggestions are provided to alleviate the energy rebound effect of Beijing. • The energy rebound effect of Beijing are quantified at the level of overall economy, industry and sector. • The overall economy of Beijing appears backfire effect and partial rebound effect. • The energy rebound effect of the tertiary industry is higher than that of the secondary industry in most years. • Sectors with low energy efficiency may have higher rebound effects. [ABSTRACT FROM AUTHOR]

• The capacity optimization model of the integrated photovoltaic- energy storage-charging station was built. • The case study bases on the data of 21 charging stations in Beijing. • The construction of the integrated charging station shows the maximum economic and environment benefit in hospital and minimum in residential. • A decline in energy storage costs increases the benefits of all-scale investments, an increase in electric vehicles promotes the benefits of small-scale investments, expansion of the peak-to-valley price distance increases the benefits of large-scale investments. The coupled photovoltaic-energy storage-charging station (PV-ES-CS) is an important approach of promoting the transition from fossil energy consumption to low-carbon energy use. However, the integrated charging station is underdeveloped. One of the key reasons for this is that there lacks the evaluation of its economic and environmental benefits. Based on the electricity load of different types of buildings and the data of electric vehicle charging stations in Beijing, this paper analyzes the economic and environmental benefits of integrated charging station developed over different scales by the capacity optimization model. Said benefits are shown to differ considerably with different locations (building types): the construction of the integrated charging station in places where the electricity load is high at night, and where the curve of daytime electricity consumption is consistent with the PV generation curve (e.g., hospitals), shows maximal economic benefits and carbon dioxide emissions reduction. In other (e.g., residential) areas, where power consumption is lower across both day and nighttime hours, the economic benefits and carbon dioxide emissions reduction are lowest. The economic and environmental benefits of the integrated charging station also markedly differ on different scales: with scale expansion, the rate of return on investment and the carbon dioxide emissions reduction first increase and then decrease. A decline in energy storage costs increases the economic benefits of all integrated charging station scales, an increase in EVs increases the economic benefits of small-scale investments, and expansion of the peak-to-valley price difference increases the economic benefits of large-scale investments. [ABSTRACT FROM AUTHOR]

• Synergistic between renewable energy and cogeneration is an ideal transition path. • Imported and exported power could reduce the CO₂ emissions of the heating system. • Investment costs mainly affect the economy of the future heating systems. • Taken Beijing as a case study, the proposed method can be applied to other regions. In response to climate change, China has set the target to achieve carbon neutrality by 2060. The large-scale development of renewable energy and the decarbonization transition of the heating system are crucial for achieving this target. Is there a synergistic effect between the heating system's decarbonization transition and the use of large-scale renewable energy sources? What effect does this synergistic development have on the CO₂ emissions and the economic costs of an energy system? This paper conducts a case study of Beijing, China, and designs three technical path scenarios for heating system decarbonization transition in the future, namely, business as usual scenario where existing policies and regulations are applied, the electricity substitution scenario where the traditional fossil fuel heating system is replaced by electric heat pump, and the synergistic development scenario where the existing thermal power units are retrofitted to replace the consumption of fossil fuels with renewable energy sources. Using the EnergyPLAN model, the hourly operation of Beijing's power and heating systems under the three scenarios are simulated. The research results are: Firstly, the proportion of local renewable energy will be over 70% in all three scenarios, and in the synergistic development scenario, the electricity and heating demand can be met by 100% renewable energy, reducing CO₂ emissions resulted from fossil fuel combustion; secondly, in view of the real-time match of power supply and demand in the power system, at times renewable energy supply would exceed demand. The rational use of the surplus renewable energy can promote further the synergistic development of decarbonization transition of heating system and renewable energy, reducing the primary energy consumption and CO₂ emissions by over 16% and 28% respectively in the future heating system; Thirdly to improve the economic benefit of a synergistic electric power and heating system, focus should be on the reduction of infrastructure construction cost rather than the reduction of fuel consumption. The proposed method can be extended to regional energy systems with more energy sectors, and can be used to formulate decarbonization transition schemes for industry, agriculture, etc. [ABSTRACT FROM AUTHOR]

Urban-scale energy model can be a crucial tool for evaluating and in turn propelling the implementation of energy efficiency and low carbon programs for buildings and cites. A variety of disputes on integrity and correctness lie in basic statistical data of China's building energy use. Inadequate information and performance gap caused by input uncertainties are the common obstacles to produce reliable energy modelling results. This paper develops a bottom-up approach to depict energy use intensity (EUI) for space heating of building in the urban level accurately, even if the data resource is inadequate and imprecise or the data are influenced by uncertain issues. A hybrid probability model is developed and the model parameters are calibrated by Bayesian inference and Markov Chain Monte Carlo simulation using data set of meter reading from 2062 stochastic-sampled heating substations in Beijing. Preparation and simulation efforts of using this energy model are discussed, following this framework and adjusting according to local climate conditions, typological building characteristics and morphological urban-scale parameters. The results show that the approach is reliable and efficient to calibrate parameters in building energy models. The urban-scale benchmarks of EUI for space heating in Beijing is successfully performed to demonstrate the proposed methodology. • Reliability of top-down and bottom-up approach are discussed in urban-scale modelling. • An urban-scale energy model is proposed even if the data are affected by uncertainty. • The framework of selection, calibration and validation of modelling are presented. • Study on urban-scale Benchmark for heating in Beijing shows how well the methodology. [ABSTRACT FROM AUTHOR]

• A framework for retrofitting existing houses to NZEBs is proposed. • The framework includes renewable system design, building envelope improvement and energy system retrofit. • The framework practicability and reliability were demonstrated in a real building retrofit. • Energy consumptions before and after retrofit were presented and compared. Nearly zero energy building is a promising solution for tackling the conflict between the reducing fossil fuel reserves and the ever-increasing energy demand. Initiatives and goals for nearly zero energy buildings have been announced in different countries, as well as a series of strategies and incentives for advocating renewable energy applications. Within this context, homeowners' energy awareness has been strengthened and more and more of them are willing to invest on energy efficiency improvement and renewable energy application. However, there is no clear method or procedure for common practitioners to follow in the practice of energy retrofit of existing buildings, more specifically existing houses. This paper fills this pressing research gap by developing a framework for retrofitting existing houses to nearly zero energy buildings. The developed framework is then implemented on a real house in Beijing to demonstrate its reliability and practicability. The innovations of this study lie in the full consideration of the actual conditions of existing houses as well as the practicability of the framework, and for the first time, the detailed energy performance measurements of a real-life house from its original inefficient condition to after being retrofitted to a nearly zero energy building are presented. The framework can be easily extended to applications of other renewables and serve as a guideline for the wide common practitioners in their real building renovation practices. [ABSTRACT FROM AUTHOR]

With Beijing's energy demand shifting to the household side in the past decade, uncovering the driving factors of Beijing's energy consumption plays an essential role for policy design. To this end, the structural changes in Beijing's residential and productive energy consumption are decomposed using the logarithmic mean Divisia index (LMDI) method in this paper. Empirical analysis is conducted focusing on Beijing's energy changes during 2009–2018. Results suggest that: (1) The contribution of residential energy consumption has exceeded that of the productive sector, meanwhile the expected decrease in both sectors' energy consumption are not observed considering the energy efficiency improvements; (2) The optimization of Beijing's energy consumption cannot solely rely on the technical development, the influence due to the upgrade of industrial structure in Beijing also matters, especially after 2011; (3) In the residential sector, all factors display stronger impacts on energy consumption changes in urban areas than in rural areas, among which the living standard is the most significant. Based on these results, suggestions are provided to promote the decrease of Beijing's energy consumption. • The structural changes in Beijing's residential and productive energy consumption are decomposed. • The phenomenon of "Jevons paradox" appear in both Beijing's productive and residential sectors. • The energy optimization in Beijing cannot solely rely on technical improvement due to the rebound effect. • All factors display stronger impacts on energy consumption changes in urban areas than in rural areas. [ABSTRACT FROM AUTHOR]