Analysis of urban energy consumption in carbon metabolic processes and its structural attributes: a case study for Beijing

IntroductionEnergy consumption and carbon emissions are major global concerns, and cities are responsible for a significant portion of these emissions. To address this problem, deep learning techniques have been applied to predict trends and influencing factors of urban energy consumption and carbon emissions, and to help formulate optimization programs and policies.MethodsIn this paper, we propose a method based on the BiLSTM-CNN-GAN model to predict urban energy consumption and carbon emissions in resource-based cities. The BiLSTMCNN-GAN model is a combination of three deep learning techniques: Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GAN). The BiLSTM component is used to process historical data and extract time series information, while the CNN component removes spatial features and local structural information in urban energy consumption and carbon emissions data. The GAN component generates simulated data of urban energy consumption and carbon emissions and optimizes the generator and discriminator models to improve the quality of generation and the accuracy of discrimination.Results and discussionThe proposed method can more accurately predict future energy consumption and carbon emission trends of resource-based cities and help formulate optimization plans and policies. By addressing the problem of urban energy efficiency and carbon emission reduction, proposed method contributes to sustainable urban development and environmental protection.

Unexpected events can have profound impacts on urban resource supply and consumption. The Great East Japan Earthquake (3.11 hereafter) triggered not only the planned blackout in the Tokyo Metropolitan Region soon after the disaster but also the energy shift to fossil fuels to recover from the disfunction of Fukushima nuclear power plants. Previous research has mainly focused on the direct energy consumption and carbon emissions of different sectors while the intensity and extensity of the impact on industries and the environment have never been empirically addressed. This study explores energy-use efficiency and carbon emissions in Tokyo from 2011 to 2015 through a lens of nexus using environmentally extended input-output analysis and community-wide carbon analytic approaches. Results show that the energy consumption is the largest exporter and importer of carbon emissions, whereas energy losses and carbon emissions caused by energy conversion and transmission are almost twice as much as those caused by the direct parts. Strong nexus effects among building and material, transportation, and energy consumption were observed. The 3.11 greatly impacted the energy structure and carbon emission patterns because of the increased consumption of coal for electricity. The share of energy consumption and carbon emissions by raw materials for construction also increased because of the increased demand for the reparation and reconstruction of buildings and transport systems. This structural change provided new scientific evidence for governments to implement decarbonization policies while preparing for unprecedented events.

With the rapid development of economy, energy demand is increasing in Hebei. Therefore, prediction of energy consumption and structure in Hebei province has importance of actual meaning significance. In this paper, total energy, coal, oil and natural gas consumption data are selected in Hebei province between 2001 and 2013. First, energy consumption and structure in Hebei province are analyzed. Second, GM(1,1)forecast model is established. Then, according to the established forecast model, energy consumption and structure between 2014 and 2021 in Hebei province is predicted. Last, related suggestions on energy optimization are put forward. The results are expected to provide important scientific basis for energy utilization and planning in Hebei province. Introduction Grey prediction is a method that can predict the systems containing uncertainties. To find the laws of system changes, original data is generating processed by identifying development trend of dissimilarity degree between system factors. Thus, data sequence with high regularity is generated. And then the corresponding differential equation model is established to predict future development trend of things. GM (1,1) prediction model with a variable and first-order differential is an important model of grey prediction. It is commonly used in energy and environment prediction because this model requires less modeling information, operates easily, forecasts precisely and is easy to test. In this paper, total energy, coal, oil and natural gas consumption data in Hebei province between 2001 and 2013 are selected as original sequence. GM (1, 1) model is constructed to predict energy consumption and structure in following 20 years in Hebei province. It hopes to provide reference and scientific basis for energy development strategy and the establishment of energy planning in Hebei. Analysis of energy consumption and structure in Hebei province The energy data in Hebei province between 2000 and 2012 are from China energy statistical yearbook. In this paper, all the energy consumption data have been converted into standard coal and the unit is ten thousand tons of standard coal. Table one shows the energy consumption and consumption structure in Hebei province. As shown in table 1, the total energy consumption in Hebei province seems to be increasing annually from 2000 to 2012 and its average annual growth rate is 7.95%. However, the speed of total energy consumption growth is different during the period and it has periodic growth characteristic. From the table, we can see that the growth speed is rapid from 2001 to 2007. Energy consumption structure in Hebei province is basically stable in recent years because of restriction on resources endowment and consumption structure of energy relying mainly on coal cannot be changed. Coal accounts for about 90 percent in energy consumption before 2011, but oil, gas and electricity such clean energy consumption occupies 10 percent of the total energy consumption. This shows that there is no variety in energy consumption structure in Hebei province and energy consumption has many defects. It depends heavily on coal which is non-renewable International Conference on Computational Science and Engineering (ICCSE 2015) © 2015. The authors Published by Atlantis Press 34 energy, so the renewable clean energy strengthened the large market demand, and the development of solar energy utilization technology has a broad prospect. Table.1 Energy consumption and consumption structure in Hebei province Year Total energy consumption Coal Oil Natural gas Electric power Total Proportion Total Proportion Total Proportion Total Proportion 2001 11195.71 10181.38 90.94 914.69 8.17 94.04 0.84 5.60 0.05 2002 12114.29 11125.76 91.84 898.88 7.42 84.80 0.70 4.85 0.04 2003 13404.53 12214.21 91.12 1092.47 8.15 93.83 0.70 4.02 0.03 2004 15297.89 14193.38 92.78 992.83 6.49 100.97 0.66 10.71 0.07 2005 17347.79 15810.78 91.14 1389.56 8.01 130.11 0.75 17.35 0.1

PDF HTML阅读 XML下载 导出引用 引用提醒 我国城市发展与能源碳排放关系的面板数据分析 DOI: 10.5846/stxb201911292591 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学重点基金项目（71533005）；国家重点研发项目（2017YFF0207303） The impact of urbanization on carbon emissions: Analysis of panel data from 158 cities in China Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:城市化与城市能耗及其碳排放密切相关，城市发展过程中的人口城市化进程和产业总量与结构调整都是能源碳排放变化的主要驱动因素。以2006-2015年全国158个地级城市的面板数据为基础，从总量变化趋势和空间变化趋势两个角度分析了研究期内的我国城市发展特征及能源碳排放特征；并利用面板计量分析方法研究了城市发展因素对城市总能耗、总能耗碳排放、单位能耗碳排放量的驱动特征。结果表明：城市化每提升0.095%，总能耗上升1%。虽然城市总能耗及能耗碳排放在降低，但是单位能耗碳排放在增加；第二产业和第三产业发展对总能耗及能耗碳排放的驱动作用大；城市第三产业的发展有利于能源结构优化调整等；并基于研究发现给出一些政策建议。 Abstract:Urbanization is closely related to urban energy consumption and associated carbon emissions. The process of population urbanization and industrial structure adjustment in urban development are the main drivers of changes in carbon emissions. Based on the panel data of 158 prefecture-level cities in China from 2006 to 2015, this study analyzes the urban development characteristics and energy carbon emission characteristics of China from total volume and spatial variation. The study uses panel measurement to analyze the driving characteristics of urban development factors on total urban energy consumption, total carbon emissions, and carbon emissions per unit energy consumption. The results show that for every 0.095% increase in urbanization, the total energy consumption increases by 1%. Although the total urban energy consumption and carbon emissions are decreasing, the carbon emissions per unit energy consumption are increasing. The total energy consumption of secondary and tertiary industries is also increasing. The development of tertiary industries in the city is beneficial for the optimization and adjustment of the energy structure. Based on the findings, some policy suggestions are proposed. 参考文献 相似文献 引证文献

Climate change is a global problem facing mankind, and achieving peak CO2 emissions and carbon neutrality is an important task for China to respond to global climate change. The quantitative evaluation of the trends of urban energy consumption and carbon emissions is a premise for achieving this goal. Therefore, from the perspective of urban expansion, this paper analyzes the complex relationship between the mutual interactions and feedback between urban population, land expansion, economic growth, energy structure and carbon emissions. STELLA simulation software is used to establish a system dynamics model of urban-level carbon emissions effects, and Changsha city is used for the case study. The simulated outputs of energy consumption and carbon emissions cover the period from 1949 to 2016. From 1949 to 2016, Changsha’s total energy consumption and carbon emissions per capita have continuously grown. The total carbon emissions increased from 0.66 Mt-CO2 to 60.95 Mt-CO2, while the per capita carbon emissions increased from 1.73 t-CO2/10,000 people to 18.3 Mt-CO2/10,000 people. The analysis of the structure of carbon emissions shows that the industrial sector accounted for the largest proportion of emissions, but it had gradually dropped from between 60% and 70% to about 40%. The carbon emissions of residential and commercial services accounted for less than 25%, and the proportion of transportation carbon emissions fluctuated greatly in 2013 and 2016. From the perspective of carbon emissions effects, carbon emissions per unit of GDP had a clear downward trend, from 186.11 t-CO2/CNY104 to 1.33 t-CO2/CNY104, and carbon emissions per unit of land showed two inflection points: one in 1961 and the other in 1996. The general trend showed an increase first, followed by a decrease, then a stabilization. There is a certain linear correlation between the compactness of urban shape and the overall trend of carbon emissions intensity, while the urban shape index has no linear correlation with the growth rate of carbon emissions. The carbon emissions assessment model constructed in this paper can be used by other municipalities, and the assessment results can provide guidance for future energy planning and decision making.

Analysis of global energy consumption inequality by using Lorenz curve

Does new energy consumption conducive to controlling fossil energy consumption and carbon emissions?-Evidence from China

This paper calculates the energy consumption and CO2 emissions of Beijing over 2005–2011 in light of the Beijing’s energy balance table and the carbon emission coefficients of IPCC. Furthermore, based on a series of energy conservation planning program issued in Beijing, the Long-range Energy Alternatives Planning System (LEAP)-BJ model is developed to study the energy consumption and CO2 emissions of Beijing’s six end-use sectors and the energy conversion sector over 2012–2030 under the BAU scenario and POL scenario. Some results are found in this research: (1) During 2005–2011, the energy consumption kept increasing, while the total CO2 emissions fluctuated obviously in 2008 and 2011. The energy structure and the industrial structure have been optimized to a certain extent. (2) If the policies are completely implemented, the POL scenario is projected to save 21.36 and 35.37 % of the total energy consumption and CO2 emissions than the BAU scenario during 2012 and 2030. (3) The POL scenario presents a more optimized energy structure compared with the BAU scenario, with the decrease of coal consumption and the increase of natural gas consumption. (4) The commerce and service sector and the energy conversion sector will become the largest contributor to energy consumption and CO2 emissions, respectively. The transport sector and the industrial sector are the two most potential sectors in energy savings and carbon reduction. In terms of subscenarios, the energy conservation in transport (TEC) is the most effective one. (5) The macroparameters, such as the GDP growth rate and the industrial structure, have great influence on the urban energy consumption and carbon emissions.

As the world's largest country regarding energy consumption and carbon emissions， analyzing China's carbon emissions and emission reduction potential is essential to the fight against global climate change. This study constructs the LEAP-China model to forecast and analyze China's carbon emissions and emission reduction potential in three dimensions： primary energy， end-use industries， and carbon emission contribution. The conclusions are as follows： ① Except for the baseline scenario， the industrial structure emission reduction， technological progress， energy structure emission reduction， and blueprint scenarios were all able to realize the goal of "peaking by 2030." ② From 2022 to 2060， carbon emissions from all industries except industry were declining. ③ The carbon emissions of various industrial sectors varied significantly according to their energy consumption， with chemicals > other industries > non-metallic mineral products industry > ferrous metal smelting and rolling processing industry > non-ferrous metal smelting and rolling processing industry > paper and paper products industry. ④ The optimization of energy structure had apparent emission reduction effects in the short term； the optimization of industrial structure was a continuous driving force for carbon emission reduction， and technological progress was a long-term driving force for carbon emission reduction. The study can provide a decision-making basis for China to realize the medium- and long-term carbon emission reduction path.

As a major energy sources province in China, Shaanxi Province ranks top three in terms of raw coal production in China and undertakes the important task of ensuring national energy supply and security. Affected by the endowment of energy resources, fossil energy accounts for a large proportion of the energy consumption structure in Shaanxi Province, and it will face huge challenges under the severe carbon emission situation in future. In order to analyze the relationship between energy consumption structure, energy efficiency and carbon emissions, the paper introduces the concept of biodiversity into the energy industry. Taking Shaanxi Province as an example, the paper calculates the energy consumption structure diversity index and analyzes the impact of energy consumption structure diversity on energy efficiency and carbon emissions in Shaanxi Province. The results shows that the diversity index and equilibrium index of energy consumption structure in Shaanxi exhibits a slow upward trend in general. In most years, energy consumption structure diversity index in Shaanxi is higher than 0.8, and the equilibrium index is higher than 0.6. The carbon emissions of energy consumption in Shaanxi generally show increasing trend, and the carbon emissions have increased from 5,064.6 tons to 21899.67 tons from 2000 to 2020. The paper also shows that Shaanxi H index is negatively correlated with total factor energy utilization efficiency in Shaanxi, and positively correlated with carbon emissions in Shaanxi. The main reason is the internal substitution of fossil energy, and the proportion of primary electricity and other energy sources is still relatively low, which leads to a higher level of carbon emissions.

Transportation is a key industry in urban energy consumption, air pollutant emissions and greenhouse gas emissions, which has a significant impact on air quality and climate change. The number of motor vehicles in Guangdong province continues to grow, with more than 18 million at the end of 2017. Under the dual pressure of energy and environmental protection, new energy vehicles, as an important carrier of “low-carbon economy”, have become the development direction of cars in Guangdong province. The Guangdong government adopts the B2B model to introduce new energy vehicles from the public transportation. In 2017, Guangdong province had 63,391 buses, of which new energy vehicles accounted for 46.2%. This study takes the implementation of new energy vehicles in the public transport system of Guangdong province as the research object, and the energy consumption of new energy vehicles and traditional vehicles were compared. Based on the CDM methodology, the changes in carbon emissions caused by the introduction of new energy vehicles in the transit system were calculated. Carbon emissions in 2020 were estimated according to the number of public buses in Guangdong province and the new energy bus planning. From 2016 to 2020, EV (electric vehicles) accounted for 63-79% of new energy vehicles in buses of Guangdong province, and HEV (hybrid electric vehicles), mainly natural gas and electric hybrid, accounted for 13-16% of the total buses. The total carbon emission of buses in 2020 was reduced by 44.6% compared with 2016, of which EV contributed the most to the emission reduction. In this study, different scenarios are set up to analyze the influence of power generation energy structure and vehicle fuel type on greenhouse gas emission reduction. It is found that power grid energy structure is a key factor affecting the carbon emission and emission reduction space of electric vehicles. The fuel type of vehicle directly affects the emission coefficient of CO2 per unit fuel, and plays an important role in carbon emission reduction.

Carbon metabolism research has attracted worldwide attention as an important way to cope with climate change, promote carbon emission reduction, increase carbon sequestration, and support low-carbon city construction. Ecological network analysis (ENA) plays an important role in network analysis and simulation of carbon metabolism. However, current studies largely focus on single elements or local processes while rarely analyzing the spatial coupling between land use and carbon metabolism. Therefore, taking Tongzhou District as an example, based on the data of land use change and energy consumption, this study constructed an analysis framework based on ENA to explore the comprehensive impact of land use changes on carbon metabolism. The results show the following: (1) From 2014 to 2020, the total carbon emissions increased year by year. Carbon emissions of other construction land (OCL) were dominant, while the carbon sequestration capacity of forest land (FL) increased by 236%. The positive carbon metabolic density remained relatively stable, while the negative carbon metabolic density decreased year by year. (2) The negative carbon flow was concentrated in the transfer of other land to OCL, accounting for 40.2% of the total negative “carbon flow.” The positive carbon flow was primarily from the transfer of other land to FL. (3) From 2014 to 2016, the spatial ecological relationships of carbon flow were dominated by exploitation and control. From 2016 to 2018, competition relationships intensified due to the expansion of the field; from 2016 to 2018, exploitation and control relationships, competition relationships, and mutualism relationships increased significantly and were evenly distributed. This study provides decision-making guidance for the subsequent formulation of government carbon emission reduction policies.

After more than two decades of negotiation, the China–Russia gas deal represents a new era of energy cooperation between China and Russia. In total, this is a win–win deal for both sides. For China, the deal will decrease energy consumption and carbon emission but will not significantly influence air quality; for Russia, it will provide a new market for its gas resources. In this study, we calculated the energy consumption, carbon emission, and particulate matter pollution (PM2.5 and PM10) in China in 2020, 2030, 2040, and 2050 under four IPCC representative concentration pathways (RCPs 8.5, 6.0, 4.5, and 2.6). We found that energy consumption and carbon emission decreased under the gas deal in RCPs 8.5, 6.0, and 4.5, although the rate of decrease slowed over time; however, in RCP 2.6, the rate of decrease of energy consumption and emission increased over time. PM2.5 and PM10 emission showed similar trends but with increasing rate, although the gas deal would mitigate air pollution in the short term. Although China’s government hopes to reduce carbon and pollutant emission under the deal, our results suggest that additional mitigation measures will be necessary to achieve this goal. Nonetheless, the reduction in carbon emission suggests that the China–Russia gas deal provides a model that other countries can follow to slow climate change.

Transmission mechanism between energy prices and carbon emissions using geographically weighted regression

PurposeThis research aims to address the critical challenge of optimizing machining processes for serial aluminum alloys, focusing on reducing carbon emissions and energy consumption while maintaining high surface quality. The study introduces the specific carbon footprint (SCF) model to evaluate CO2 emissions per unit material removed, aiming to enhance sustainable production practices in mass manufacturing.Design/methodology/approachUsing response surface methodology (RSM), experiments were conducted on 5,000, 6,000 and 7,000 series aluminum alloys to assess the impact of cutting speed and feed rate on surface quality, energy consumption and carbon footprint. Energy usage data were collected, and analysis of variance was used to identify the contributions of process parameters.FindingsThe results revealed that feed rate is the most influential factor, contributing 51.8% to the SCF, followed by cutting speed at 32%. Optimal conditions reduced CO2 emissions by 37%, cutting the carbon footprint from 516.4 tons to 325 tons annually. Among the materials tested, the 6,000 series exhibited the best machinability, balancing low energy consumption and high surface quality.Research limitations/implicationsThe proposed SCF model serves as a novel metric for sustainable manufacturing, enabling precise evaluation of carbon emissions in machining processes. This work establishes a benchmark for optimizing machining parameters, significantly reducing environmental impact in mass production scenarios.Originality/valueThis study pioneers the integration of SCF into machining optimization and offers actionable insights for sustainable manufacturing. It highlights the potential of using RSM to simultaneously optimize energy efficiency, surface quality and carbon emissions, providing a valuable framework for future research and industrial applications.