Calculation of Energy Consumption and Carbon Emissions in the Construction Stage of Large Public Buildings and an Analysis of Influencing Factors Based on an Improved STIRPAT Model

This study takes Kunming City, Yunnan Province, China as the research area, to provide reference basis for revealing the change law of land use structure and energy consumption and carbon emissions in Kunming, optimizing land use structure and realizing the development of low-carbon city. Based on the data of land use structure and energy consumption in Kunming from 1997 to 2017, based on the estimation of total energy consumption carbon emissions, carbon intensity and per capita carbon emissions, the correlation between land use structure and energy consumption carbon emissions in Kunming has been calculated and analyzed in the past 20 years. Results: 1) The total amount of carbon emissions in Kunming has increased significantly in the past 20 years. It increased from 34.46 × 105 t to 95.09 × 105 t, an increase of about 2.8 times. 2) The types of land use with the highest correlation between land use structure and total carbon emissions of energy consumption, carbon emission intensity and per capita carbon emissions are urban and village and industrial and mining land (0.8258), cultivated land (0.8733) and garden land (0.7971) respectively. 3) The correlation between construction land and total carbon emissions is greater than that of agricultural land. Conclusion: There is a close correlation between land use structure and carbon emissions from energy consumption in Kunming.

This article focuses on the production stage of industrialized building components. Through the analysis of the measured data, it is concluded that the unilateral carbon emissions generated by the consumption of natural gas in the steam curing stage of the northern factory account for more than 80% of the production process. The difference in natural gas consumption is the energy consumption and carbon emissions in the component production stage. The energy consumption and carbon emissions of the Nanfang (Longxin) plant in summer are higher than the transition season and winter; the power consumption and carbon emissions of auxiliary equipment in summer are close to 20% of the total carbon emissions during the production process, which is the season for energy consumption and carbon emissions in the component production stage The main reason for sexual differences.

The carbon dioxide is the major component of greenhouse gas in energy consumption. Because of different energy varieties and consumption patterns carbon emissions are different. Therefore, the estimate of carbon emissions should be calculated one by one according to different energy variety which bases on the clear range of energy consumption counting. In accordance with the carbon emission factors recommended by IPCC, this article uses one-by-one calculation method to sum the carbon emissions in Jinan City since 1990. Then the total carbon emissions, carbon emissions per capita, carbon emission intensity and sub-sectors carbon emissions were compared respectively.

Urban water resources management with energy constraint and carbon emission intensity under uncertainty: A dual objective optimization model

In order to calculate the carbon emissions in the construction process to achieve low-carbon buildings and low-carbon construction, the author puts forward the calculation and evaluation of building thermal energy consumption and carbon emissions based on building information modeling (BIM) technology. The author first proposed the important value and application of BIM technology in energy consumption evaluation of green buildings, taking a gymnasium as an example, a carbon emission accounting system for building construction and installation process is established based on BIM technology, and the carbon emissions in building construction and installation process are calculated and analyzed. The results show that the carbon emission during the construction and installation of a gymnasium is 766300 tons, of which the carbon emission caused by building materials is 737200 tons, the carbon emission caused by mechanical equipment is 4500 tons, and that caused by office and living is 34500 tons, accounting for 94.90%, 0.59%, and 4.51%, respectively. In conclusion through data analysis, determine the largest carbon emission source in the construction process, and then propose targeted carbon emission reduction measures in the construction process of the construction industry.

This article studies the energy carbon emission (ECE) evaluation problem for enterprises, and designs ECE-codes to conduct multi-factor grading evaluation of the ECE. The ECE evaluation results of enterprises need to be displayed to non-professionals such as consumers. Therefore, in addition to being able to characterize the ECE level, the ECE evaluation results of enterprise should be intuitive and easy to understand. For this purpose, the ECE-codes are designed, including the horizontal identification code (HIC), the efficiency identification code (EIC), and the neutralization identification code (NIC). Define carbon emission intensity (CEI) as the ECEs required to produce an unit of profit. The HIC is formed by dividing the CEI of enterprises into three levels, and is used to grade an enterprise's carbon emission level in the entire industrial sector. The EIC is formed through five-level evaluation of CEI in sector (CEIS), which is used to measure the CEI level of an enterprise in its sector. The CEIS is the ratio of CEI to the average value of the sector CEI. NIC is used to display an enterprise's carbon neutrality process, which is the ratio of carbon neutrality to total carbon emissions. To achieve effective grading of CEI and CEIS, a Gaussian mixture model (GMM) is established to describe the distribution of the CEI and CEIS, and the model parameters are identified by the expectation maximization (EM) algorithm. Then, the grading thresholds can be obtained according to the GMM probability density function with given percentage parameters. The effectiveness of the ECE-codes based grading evaluation method is verified by applying this method to the carbon emission evaluation of the registered enterprises in Huzhou, china.

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

The calculation methodology of transport carbon emissions, based on the methodology recommended by Intergovernmental Panel on Climate Change (IPCC) and the energy consumption statistics of provincial transport industry in China, is proposed. By using the methodology, the energy consumption and carbon emissions of highway, waterway and urban passenger transport from 2005 to 2012 of Jiangsu Province are calculated and evaluated. And the developing trends and main features from the perspectives of the total amount of transport energy consumption and carbon emissions, the proportional of both various energy types and various transport modes in the energy consumption, the energy intensity and carbon dioxide intensity, are systematically analyzed. Finally, some policy implications of low-carbon transport development were conclusively put forward, including reducing energy intensity and carbon intensity as the core focus, the highway transport as the breakthrough point, optimizing the integrated transport system structure and developing of public transport in priority as the strategic orientation, developing clean and low-carbon energy as an important way, etc. The research methodology and results can provide references for decision-making and management of the relevant provinces and cities on low-carbon transport development.

In this study, the existing methods for calculating energy consumption and carbon emissions are reviewed. Afterwards, a generalized life cycle approach is proposed to calculate carbon emissions and energy consumption of asphalt pavement by considering the maintenance and repair processes. Besides, by taking into account of the differences in maintenance methods that affect the road life, annual carbon emissions and energy consumption are suggested as metrics for the measurement of carbon emissions and energy consumption. On this basis, 12 projects with different road surface structures and regions are uniformly selected nationwide. This study deeply explores the energy consumption and carbon emissions of asphalt pavement from the impacts of calculation boundary, environment, construction, and energy-saving and emissions-reduction technology impacts. According to the results, annual energy consumption and carbon emissions of asphalt pavement are greatly affected in the maintenance and repair process, with the maximum annual differences of 37% and 41%, respectively. As indicated by the Spearman correlation analysis road surface structure has a remarkable impact on energy consumption and carbon emissions, and reasonable road surface design should be emphasized in the future road design. Furthermore, by comparing with the hot mix asphalt technology, the emulsified asphalt cold recycling technology can decrease energy consumption and carbon emissions by at least 50%. This study provides theoretical support for the future asphalt pavement structure design, as well as the emissions reduction technology promotion and application of the emissions reduction technology.

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

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.

The establishment of low-carbon cities has been suggested all over the World, since cities are key drivers of energy usage and the associated carbon emissions. This paper presents a scenario analysis of future energy consumption and carbon emissions for the city of Beijing. The Long-range Energy Alternatives Planning (LEAP) model is used to simulate a range of pathways and to analyze how these would change energy consumption and carbon emissions from 2007 to 2030. Three scenarios have been designed to describe future energy strategies in relation to the development of Beijing city, namely a reference scenario (RS), control scenario (CS), and integrated scenario (IS). The results show that under the IS the total energy demand in Beijing is expected to reach 88.61 million tonnes coal equivalent (Mtce) by 2030 (59.32 Mtce in 2007), 55.82% and 32.72% lower than the values under the RS and the CS, respectively. The total carbon emissions in 2030 under the IS, although higher than the 2007 level, will be 62.22% and 40.27% lower than under the RS and the CS, respectively, with emissions peaking in 2026 and declining afterwards. In terms of the potential for reduction of energy consumption and carbon emissions, the industrial sector will continue to act as the largest contributor under the IS and CS compared with the RS, while the building and transport sectors are identified as promising fields for achieving effective control of energy consumption and carbon emissions over the next two decades. The calculation results show that an integrated package of measures is the most effective in terms of energy savings and carbon emissions mitigation, although it also faces the largest challenge to achieve the related targets.

In recent years, the environmental problems caused by excessive carbon emissions from energy sources have become increasingly serious, which not only aggravates the climate change caused by the greenhouse effect but also seriously restricts the sustainable development of Chinese economy. An attempt is made in this paper to use energy consumption method and input-output method to study the carbon emission structure of China's energy system and industry in 2015 from two perspectives, namely China's energy supply side and energy demand side, by taking into account the two factors of energy invest in gross capital formation and export. The results show that neglecting these two factors will lead to underestimation of intermediate use carbon emissions and overestimation of final use carbon emissions. On energy supply side, the carbon emission structure of China's energy system is still dominated by high-carbon energy (raw coal, coke, diesel, and fuel oil, etc.), accounting for more than 70% of total energy carbon emissions; on the contrary, the natural gas such as clean energy accounts for only 3.45% of total energy carbon emissions, indicating that the energy consumption structure optimization and emission reduction gap of China's energy supply side are still substantial. On energy demand side, the final use (direct consumption by residents and government) produces less carbon emissions, while the intermediate use (production by enterprises) produces more than 90% of the total energy carbon emissions. Fossil energy, power sector, heavy industry, chemical industry, and transportation belong to industries with larger carbon emissions and lower carbon emission efficiency, while agriculture, construction, light industry, and service belong to industries with fewer carbon emissions and higher carbon emission efficiency. This means that the optimization of industrial structure is conducive to slowing down the growth of energy carbon emissions on the demand side.

Excessive carbon emissions will cause the greenhouse effect and global warming, which is not conducive to environmental protection and sustainable development. In order to realize the goal of “carbon peak and carbon neutrality” as soon as possible, this paper utilizes the methodology provided by the IPCC to measure the carbon emissions and carbon intensity of China’s energy consumption. The classification method of carbon emission and the kernel density function method are used to explore the spatial and temporal evolution of regional carbon emissions. Based on the Log Mean Divided Index (LMDI) method, the drivers of China’s energy carbon emissions are measured. Based on the Tapio index function and the catch-up decoupling model, the decoupling status of Chinese provinces and the development gap with the benchmark provinces are examined. The results show that (1) China’s total energy carbon emissions show a “rising-declining-rising” trend from 2005 to 2021, and reach the first peak in 2013, totaling 1,484,984.406 million metric tons. China’s Hebei, Shanxi, and Shandong provinces have the highest energy carbon emissions. (2) China’s energy carbon emissions are influenced by multiple factors, and the contribution of each factor to energy carbon emissions is in the following order: economic development effect > energy intensity effect > energy structure effect > population size effect. (3) China’s catch-up provinces develop their economies at the expense of the environment and energy consumption.

Household carbon emissions are important components of total carbon emissions. The consumer side of energy-saving emissions reduction is an essential factor in reducing carbon emissions. In this paper, the carbon emissions coefficient method and Consumer Lifestyle Approach (CLA) were used to calculate the total carbon emissions of households in 30 provinces of China from 2006 to 2015, and based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the factors influencing the total carbon emissions of households were analyzed. The results indicated that, first, over the past ten years, the energy and products carbon emissions from China’s households have demonstrated a rapid growth trend and that regional distributions present obvious differences. Second, China’s energy carbon emissions due to household consumption primarily derived from the residents’ consumption of electricity and coal; China’s products household carbon emissions primarily derived from residents’ consumption of the high carbon emission categories: residences, food, transportation and communications. Third, in terms of influencing factors, the number of households in China plays a significant role in the total carbon emissions of China’s households. The ratio of children 0–14 years old and gender ratio (female = 100) are two factors that reflect the demographic structure, have significant effects on the total carbon emissions of China’s households, and are all positive. Gross Domestic Product (GDP) per capita plays a role in boosting the total carbon emissions of China’s households. The effect of the carbon emission intensity on total household carbon emissions is positive. The industrial structure (the proportion of secondary industries’ added value to the regional GDP) has curbed the growth of total carbon emissions from China’s household consumption. The results of this study provide data to support the assessment of the total carbon emissions of China’s households and provide a reasonable reference that the government can use to formulate energy-saving and emission-reduction measures.