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Highlights • The majority of carbon emissions derived from indirect fraction of energy consumption in Beijing. • Life expectancy is reduced in areas where a higher amount of coal is burnt for household consumption. • Socio-economic structure changes play a major role in the observed energy and emission trends. • Search of optimal solutions for energy-efficient and low carbon private will continue to be crucial. Abstract Household carbon emissions are major contributors to global emissions that is increasing recognized. In China, approximately 20 million rural populations transfer to the urban areas annually, which results in a lot of new infrastructure construction and consumer requirements. This paper quantifies the numbers and trends of urban and rural resident on energy use and carbon footprint in Beijing from 1996 to 2011 by analysis of Consumer Lifestyle Approach (CLA) and carbon footprint. It provides a better understanding of complete energy consumption and carbon emissions profile in Beijing. The results show that for the Beijing residents, when direct and indirect energy consumption and CO 2 emissions are taken into account, the total household energy consumption proportion compared to the city increases from 22.7% to 59.2%, and the total household CO 2 emissions proportion increases from 32.2% to 68.8% in the research period. In the urbanization process, total energy consumption and carbon emissions of Beijing urban residents are significantly higher than those of rural residents. However, the transformation of a rural Beijing resident into an urban Beijing resident would not create a large difference between urban and rural areas in the per-capita energy consumption and per-capita carbon footprint. Results are assessed with respect to the available scientific literature, as well as potential available technological and policy solutions. Since the implementation that different policy instruments can mitigate carbon emissions, governments are gradually combining policy measures for the same technology. The results in this paper may help promote alternative consumption strategies in household sector which could entail more reasonable resource distribution and effective reduction of both urban and rural environmental influences. [ABSTRACT FROM AUTHOR]

Energy consumption has promoted a continuous development for economy and society, but it has also brought serious environmental pollution problems which endanger public health. In order to focus on the impact of PM 2.5 emissions from future energy consumption on human health better, this paper estimated PM 2.5 emissions caused by energy consumption in Beijing in 2020,2025 and 2030 by setting the baseline scenario, emission reduction scenario, intensified emission reduction scenario and the data of the base year 2015, which will be 162.6 thousand tons, 195.9 thousand tons and 235.9 thousand tons in the baseline scenario, 143.1 thousand tons, 151.8 thousand tons and 160.9 thousand tons under the emission reduction scenario, and 132.6 thousand tons, 130.2 thousand tons, 127.9 thousand tons under the intensified emission reduction scenario. Finally the paper measured the number of different health effect ends caused by PM 2.5 emissions and the economic loss. The results showed that there was a rapid increase, a mild increase and a moderate decrease in energy consumption and PM 2.5 emissions in Beijing under the three scenarios from 2015 to 2030. Besides, under the same scenario and the same concentration level baseline, the number of the internal medicine (over the age of 15) was the largest, and the economic loss caused by total death rate was the biggest. This article provides suggestions to reduce PM 2.5 emissions and public health effect ends for Beijing in the future. [ABSTRACT FROM AUTHOR]

Even though renewable energy development has gained momentum in China, thermal power generation still accounts for approximately 70% of the county's total power generation serving as the major source of carbon dioxide (CO 2) emissions in China. Facing the challenges of meeting 2030 peak target of CO 2 emission and realizing the coordinated development of thermal power generation in Beijing-Tianjin-Hebei region, this paper applies generalized Divisia Index Method (GDIM) to decompose the dynamics in the relevant CO 2 emission. The effects of five factors including electricity demand, energy consumption, technology, energy efficiency and energy-mix are considered. The decomposition suggests that electricity demand is the primary factor driving the CO 2 emission up, whereas technology effect decreases CO 2 emission the most. Given the significant roles of technology, energy-mix and energy efficiency in CO 2 emissions reduction, seven scenarios are designed to identify the optimal coordinated development pathway for thermal power generation in Beijing-Tianjin-Hebei region. Through upgrading energy structure and/or enhancing energy efficiency, the thermal power generation in Beijing-Tianjin-Hebei region can achieve coordinated development and realize the 2030 peak target under four scenarios. The detailed development pathways for CO 2 emissions and specific policy implications for Beijing, Tianjin and Hebei are provided to further govern CO 2 emissions and maintain sustainable development. • Proposed a five-factor Generalized Divisia Index Model (GDIM) for CO 2 emission analysis. • Analyzed the CO 2 from thermal power generation in China's Beijing-Tianjin-Hebei region. • Electricity demand and technology contributed the most to the positive and negative changes of CO 2 , respectively. • Designed seven thermal generation development scenarios to study the CO 2 change until 2030. • The 2030 CO 2 peak target can be achieved under four scenarios. [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]

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]

Rural energy consumption significantly affects the economy of a country and plays a vital role in the rural residents’ lives. This paper analyzed the association of the rural household energy consumption with their demands on renewable energy systems and contributions to the reduction of CO 2 , TSP (total suspended particulate), and PM 2.5 emissions in Zhangziying town of Daxing district of Beijing. This system analysis aimed to evaluate the sustainability of energy supply and economic development in rural area of Beijing by taking Zhangziying town as an example. Specifically, in this paper energy consumed by the rural households was investigated in terms of energy sources (coal, biogas, straw gas, LPG, electricity and firewood), utilization, choices, costs and systems. The results show that coal is still the major energy source for heating during winter, which constitutes that largest portion of the household energy consumption. With the development of renewable energy, the utilization of coal, LPG and firewood for cooking has been replaced by renewable energy such as biogas and straw gas, which are coupled with a higher heating value and lower price. Meanwhile, this study also suggests that the renewable energy generated in the surveyed villages is equivalent to 32,863.68 kgce and could annually reduce 81,929.15 kgCO 2 emissions, 22,347.30 kg TSP, and 8938.92 kg PM 2.5 emissions. The structure of rural household energy consumption of Zhangziying town is sustainable and environmentally sound. The structure of rural household energy consumption is undergoing a transformation from traditional low-efficiency biomass domination to integrated consumption of traditional and renewable energy sources. Renewable energy is not only a solution to the energy shortage problem in rural areas, but also helps conserve fossil resources and protect the environment. [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]

The facilitation of regional trade has boosted the fragmentation in global supply chain, integrating the production, energy and other ecological factors across the regional boundaries by virtue of vertical specialization. The consequences of this trend on urban energy consumption are firstly analyzed by constructing multiregional input–output table and decomposing the value added export in Beijing. Energy consumption per unit of GDP in Beijing is only half of that per unit of value added after adjusting the export by virtue of decomposition techniques and Beijing displays no obvious advantages in energy usage when considering the value added processes. Energy intensive parts of production are relocated outside the metropolis by the downstream and upstream participation in global areas. The results of value added export for energy consumption reveal that the vertical specialization in this metropolis slashes the amount of energy consumption in the city but raises that in the outside. The unified framework by integration of the value added export and energy consumption in this paper could be extended to other related topics. [ABSTRACT FROM AUTHOR]

Energy consumption has always been a central issue for sustainable urban assessment and planning. Different forms of energy analysis can provide various insights for energy policy making. This paper brought together three approaches for energy consumption accounting, i.e., energy flow analysis (EFA), input–output analysis (IOA) and ecological network analysis (ENA), and compared their different perspectives and the policy implications for urban energy use. Beijing was used to exemplify the different energy analysis processes, and the 42 economic sectors of the city were aggregated into seven components. It was determined that EFA quantifies both the primary and final energy consumption of the urban components by tracking the different types of fuel used by the urban economy. IOA accounts for the embodied energy consumption (direct and indirect) used to produce goods and services in the city, whereas the control analysis of ENA quantifies the specific embodied energy that is regulated by the activities within the city’s boundary. The network control analysis can also be applied to determining which economic sectors drive the energy consumption and to what extent these sectors are dependent on each other for energy. So-called “controlled energy” is a new concept that adds to the analysis of urban energy consumption, indicating the adjustable energy consumed by sectors. The integration of insights from all three accounting perspectives further our understanding of sustainable energy use in cities. [ABSTRACT FROM AUTHOR]

The ever-increasing electricity price and energy consumption in high-speed railway industry push railway companies to seek a promising way to realize their sustainable developments. Making full use of the solar resource along with high-speed railways can be a potential solution to cut the electricity bill, bring more profit to railway companies and realize the decarbonization of high-speed railway industry. This paper studies the optimal planning of distributed photovoltaic generation (DPVG) and energy storage system (ESS) for the traction power supply system (TPSS) of high-speed railway. A quantitative method is proposed to study the time and space characteristics of photovoltaic generation and electricity demand of high-speed trains. An integrated cost-benefit analysis framework is developed to evaluate the effect of DPVG and ESS on the economy of TPSS. To derive the optimal planning scheme and energy management strategy of DPVG and ESS, a mathematical programming model with the objective of minimizing the total cost is proposed to seek the most economical solution. A hybrid global optimal solution approach is developed to solve the model. A real-world case of Beijing-Baoding high-speed railway in China is used to illustrate the capability and characteristics of the proposed model. The computational results show that DPVG is able to supply 32.5 % electricity demand of high-speed trains. The integration of DPVG and ESS can help railway company save 4.2 million CNY each year in Beijing-Baoding high-speed railway. This paper demonstrates the potential and applicability of DPVG and ESS in high-speed railway industry. • A novel distributed photovoltaic generation model is proposed for the traction power supply system of high-speed railway. • A global optimal algorithm is provided to derive the most economical planning scheme and energy-management strategy. • Photovoltaic generation can help Beijing-Baoding high-speed railway save 4.2 million China Yuan each year. • Solar energy is able to supply 32.5% electricity demand of Beijing-Baoding high-speed railway. [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 the rapid growth of urbanisation and associated vehicle numbers in China, urban transportation has become responsible for a large increase in the country's energy consumption and, consequently, its air pollution levels. Beijing has been making significant efforts to reduce transportation sector energy consumption and pollution. However, owing to the existence of a large number of private cars, the implementation of effective energy consumption and emission reduction measures has been difficult to achieve. This paper reviews the policies and regulations that have so far been developed to tackle Beijing's transportation sector issues. Perspective and analysis are given on the specific policies and regulations that have been employed, focusing on the overarching regulation mechanism, public transportation infrastructure, vehicle technology-related measures, vehicle activity-related measures and eco-driving -related measures, relevant experiences and lessons are summarised. A key finding concludes that the implementation of driving restriction policy can restrain pollutants emissions significantly, it shows energy consumption and emission reduction should not only be applied to public transportation, but the effective control of emissions from private cars should also attract more attention. To better control energy consumption of urban transport, various measures and solutions are proposed and discussed, including the further popularizing of eco-driving and the widespread application of e-mobility. Specific proposals for developing urban transportation policy are also discussed. [ABSTRACT FROM AUTHOR]

This paper investigates whether the China ETS policy has achieved carbon emission reduction at the expense of economic development. Moreover, we are interested in unmasking the role of the institutional factors adopted by the ETS pilots in their ETS effects. Using the province-level panel data during the 2008–2016 period. we employ the DID model to compare carbon emissions and economic development between the ETS and non-ETS regions and between the pre- and post-ETS periods. Some novel empirical findings emerge. First, compared with the non-ETS areas, the ETS policy has significantly reduced carbon emission in the ETS areas. This emission reduction has not come at the cost of economic development. Second, the ETS policy leads to a decline in carbon intensity and fossil fuel energy consumption relative to all energy types. Lastly, some heterogeneity across markets arise. The Beijing carbon market performs the best among all pilots in terms of achieving targets of carbon reductions, followed by the Hubei carbon market. [ABSTRACT FROM AUTHOR]

• Long-term energy use and CO 2 emissions from urban buildings are estimated. • System dynamics modeling is performed to assess the holistic effects of multiple policies. • Potentials of different policies in reducing CO 2 emissions from urban buildings are investigated. • Inefficiency and interference due to the simultaneous application of multiple policies are identified. • Suggestions for systematically improving and enforcing multiple policies are provided. To reduce energy-related CO 2 emissions from the urban building sector, multiple diverse energy efficiency policies are often simultaneously implemented by different government entities. Thus, evaluating and quantifying the effects of these policies are important for improving their efficacy and prioritizing them. In this paper, a projection of the long-term CO 2 emissions from city-scale urban buildings whose energy use and construction are governed by multiple diverse policies is presented. A system dynamics (SD) model is established to simulate the causal path through which different policies directly and indirectly affect the energy use of and CO 2 emissions from buildings in the urban building stock, and the model is applied to Beijing, China. The results show that under the existing set of policies, the primary energy use by urban buildings in Beijing will peak around 2025 and that energy-related CO 2 emissions will peak around 2022. The model also reveals interference between the policy for promoting green building construction and the policy limiting the urban building stock area. Targeted measures for improving and prioritizing these policies are suggested, and implications for policy design and implementations are proposed. The framework developed in this study can be used not only to project long-term energy-related emissions from building sectors but also to evaluate the effects of policies on emissions. Suggestions for adapting the model to more general cases are provided. [ABSTRACT FROM AUTHOR]

• A 3-level fragment division method is proposed for actual vehicle data processing. • The influencing factors of electric vehicle energy consumption are discussed. • A novel data-driven energy consumption prediction framework is proposed. • The driving conditions prediction is integrated in the proposed framework. With increasing mass-adoption of electric vehicles, the energy consumption has become a key performance index to electric vehicle drivers, automakers and policy-makers. Accurate and real-time energy consumption prediction under real-world driving conditions is essential for alleviating the 'range anxiety' and can provide support for optimal battery sizing, energy-efficient route planning and charging infrastructures operation. In this paper, real-world driving data collected from fifty-five electric taxis in Beijing city are obtained and divided into three-level driving fragments. The influencing factors of energy consumption, including vehicle-, environment-, and driver-related factors, are extracted and studied. With the extracted key influencing factors, a novel machine learning-based energy consumption prediction framework integrated with driving condition prediction is proposed and used in actual energy consumption prediction. The real-world trip test results show that a root mean squared error of 0.159kWh (RMSE) and a mean absolute percentage error 12.68% (MAPE) are reached, the RMSE and the MAPE are respectively reduced by 32.05% and by 30.14% compared to the conventional method. [ABSTRACT FROM AUTHOR]

• A new comprehensive control method based on Model Predictive Control is proposed. • A new dynamic equation of Continuously Variable Transmission is proposed. • Take engine torque and clamping force as control objectives. • The method can improve transmission efficiency by 8.92% and fuel economy by 4.9%. This paper proposes an optimal comprehensive control framework to maximize the powertrain transmission efficiency and fuel economy meeting vehicle owners' travel needs. The proposed control framework consists of vehicle layers and verification. In the vehicle layer, creatively, each set of automotive Continuously Variable Transmission (CVT) supply equipment is equipped with original comprehensive controller that estimates engine fuel consumption and CVT efficiency based on a newly dynamic equation of CVT, engine torque, clamping force, and upcoming trip information. The controller obtains the optimal input of engine torque and clamping force by repeatedly solving the optimization problem of each sampling point in real-time. In the verification layer, with the powertrain system model received from the comprehensive controller, the robustness was verified by bench and hardware-in-the-loop simulation platform. The proposed comprehensive optimization approach can enhance accuracy and solve the multi-objective control problems compared with traditional methods. It is also scalable to the expanding CVT fleet and robust to uncertainties future system condition and in further to meet the vehicle owner's travel comfort and economy. The proposed method is studied using a prototypical driving platform developed at State Key Laboratory of Automotive Safety and Energy of Tsinghua University and detailed trip information extracted from 2019 BEIJING Transport Annual Report Survey. The main contribution of this study is to explore a novel way to optimize the comprehensive transmission efficiency of engine and CVT, compared with the Proportional Integral Derivative (PID) control, the comprehensive controller can improve the transmission efficiency by 8.92%, and reduce the fuel consumption by 4.9%, it also can save about 90 million liters of fuel and 70 million US dollar for CVT vehicles in Beijing. [ABSTRACT FROM AUTHOR]

Along with rapid urbanization and economic development, urban household consumption has experienced a remarkable change, leading to a corresponding increase in direct and indirect carbon emissions. A systematic accounting of household carbon footprints (HCFs) in Beijing during 2002, 2007, and 2012 using a nested input-output model is presented in this paper. The results indicate that the per capita carbon footprint from urban household consumption in Beijing increased from 3054.05 kg in 2002–4611.12 kg in 2012, of which the indirect emissions were the majority. In terms of consumption structure, food, housing, transportation and communication, and education and entertainment were the four dominant sources contributing to the indirect urban HCFs in Beijing. With respect to the source, local Beijing was the largest contributor for itself materials and service provisions in 2002 and 2007, while the domestic source became the largest contributor in 2012, accounting for 55.08% of the total indirect household carbon emissions. Moreover, we also discovered that families with high income level had much higher HCFs compared to those with low income level. Given these considerations, transitions towards low-carbon household consumption are needed so as to provide equality and sustainability. • Nested input-output model is adopted to calculate household carbon footprints (HCFs). • Per capita HCFs in Beijing increased from 3054.05 kg in 2002–4611.12 kg in 2012. • Indirect emission constituted the majority of HCFs, accounting for 63.69% in 2012. • Income level has great impacts on HCFs due to energy intensive consumption pattern. [ABSTRACT FROM AUTHOR]

• Studying on dynamic ecological footprint (EF) in Jing-Jin-Ji from 1996 to 2020. • Six impact factors for driving EF is decomposed. • The EF in Jing-Jin-Ji will increase of 10% from 2015 to 2020. The ecological footprint (EF) describes the complex relationship between the eco-environment and economic development. EF dynamics can better reflect the appropriation of natural resources in various countries or regions compared with previous studies. This paper identified six dominant factors including population, per capita GDP, three major industrial added values, and energy consumption using grey correlation model for the EF changes of the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 1996 to 2015. Then, we predicted the EF of the Jing-Jin-Ji region from 2016 to 2020 based on support vector machine model. (1) Since 1996, the EF and per capita EF of the overall Jing-Jin-Ji region have increased, of which those of Hebei Province and Tianjin increased while Beijing decreased. (2) Energy consumption dominated the EF of Jing-Jin-Ji region. The population of Beijing had a high correlation coefficient of 0.735 with the local EF, and the degree of correlation between the EF and per capita GDP was 0.812 in Hebei Province. In Tianjin, the added value of the tertiary industry was closely correlated with its EF, with a correlation coefficient of 0.741. (3) The EF of the Jing-Jin-Ji region will reach 778.30 million hectares by 2020, an increase of 10% compared with the value in 2015. Finally, suggestions for the development of industrial structure and energy consumption are listed for the sustainable development of Jing-Jin-Ji region. [ABSTRACT FROM AUTHOR]

Carbon emissions and energy consumption have serious impacts on humans and ecosystems. This paper investigates the effects of industrial reconstructuring on energy conservation and emission reduction. A multi-objective optimization model was established. The model classifies industrial sectors into four groups according to their carbon emission levels and contributions to economic growth, then non dominated sorting genetic algorithm is applied to solve the model and the Pareto frontier is obtained. The best solution is selected from the Pareto frontier by using a super data envelopment analysis model which measures the degree of coordination between economy and environment. The model is used to analyze the effects of industrial reconstructuring in Beijing during 2018–2020. The results show that industrial reconstructuring enabled economic growth to reach the government's planned rate while the carbon intensity and energy intensity surpassed the goal of the 13th Five-Year Plan. This case can provide decision-making basis for the sustainable development of ecology and economy in other regions. Fig. 6. Pareto front in Beijing region in 2020 The optimal Pareto solution for carbon emissions, energy consumptions and GDP targets after industrial structure reconstructuring. Image 1 • Classifying industrial sectors into four groups according to carbon emission levels and economic growth contributions. • A multi-objective optimization model was established and solved by the genetic algorithm. • A super DEA model evaluates the final plan which reflects the coordination of the economy and environment. • The analysis of each group can help achieving maximum benefits of economic and environment. [ABSTRACT FROM AUTHOR]