Analysis and forecast of residential building energy consumption in Chongqing on carbon emissions

Carbon emissions mainly result from energy consumption. Carbon emissions inevitably will increase to some extent with economic expansion and rising energy consumption. We introduce a gray theory of quantitative analysis of the energy consumption of residential buildings in Chongqing,China,on the impact of carbon emission factors. Three impacts are analyzed,namely per capita residential housing area,domestic water consumption and the rate of air conditioner ownership per 100 urban households. The gray prediction model established using the Chongqing carbon emission-residential building energy consumption forecast model is sufficiently accurate to achieve a measure of feasibility and applicability.

Residential Energy Consumption and Associated Carbon Emission in Forest Rural Area in China: A Case Study in Weichang County

Rural energy consumption in China has increased dramatically in the last decades, and has become a significant contributor of carbon emissions. Yet there is limited data on energy consumption patterns and their evolution in forest rural areas of China. In order to bridge this gap, we report the findings of field surveys in forest villages in Weichang County as a case study of rural energy consumption in northern China. We found that the residential energy consumption per household is 3313 kgce yr^-1 （kilogram standard coal equivalent per year）, with energy content of 9.7×lO7 kJ yr^-1, including 1783 kgce yr^-1 from coal, 1386 kgce yr^-1 from fuel wood, 96 kgce yr^-1 from electricity, and 49 kgce yr^-1 from LPG. Per capita consumption is 909 kgce yr^-1 and its energy content is 2.7×lO7 kJ yr^-1. Due to a total energy utilization efficiency of 24.6%, all the consumed energy can only supply about 2.4×107 kJ yr^-1 of efficient energy content. Secondly, household energy consumption is partitioned into 2614 kgce yr^-1 for heating, 616 kgce yr^-1 for cooking, and 117 kgce yr^-1 for home appliances. Thirdly, the associated carbon emissions oer household are 2556 kzC yr^-1, includinz1022 kgC yr^-1 from unutilized fuel wood （90% of the total fuel wood）. The rest of emissions come from the use of electricity （212 kgC yr^-1, coal （13Ol kgC yr^-1 and LPG （21 kgC yr^-1. Fourthly, local climate, family size and household income have strong influences on rural residential energy consumption. Changes in storage and utilization practices of fuel can lead to the lO%-30% increase in the efficiency of fuel wood use, leading to reduced energy consumption by 924 kgce yr^-1 per household （27.9% reduction） and 9Ol kgC yr^-1 of carbon emissions （35-3% reduction）.

Analysis on Carbon Emissions from Energy Consumption in Agriculture and Reduction Measures in Guangdong Province

[Objective] The aim was to study CO2 emissions from energy consumption in agricultural production in Guangdong Province and put forward feasible reduction measures.[Method] Based on the data from China Energy Statistical Yearbook and Guangdong Statistical Yearbook,CO2 emissions from agricultural energy use in Guangdong Province from 2000 to 2009 was estimated by using the formula of carbon emissions recommended by Intergovernmental Panel on Climate Change (IPCC),and corresponding reduction measures were put forward.[Result] With the rapid increase of agricultural output and energy consumption,CO2 emissions from energy consumption in agricultural production in Guangdong Province showed increasing trend from 2000 to 2009,that is to say,increasing from 423.63×104 t C million tons in 2000 to 605.99×104 t C in 2009,with annual growth rate of 4.1%.Meanwhile,carbon emissions intensity during energy consumption in agriculture went down in recent ten years,in other words,decreasing from 0.424 t C/×104 yuan in 2000 to 0.301 t C/×104 yuan in 2009,and its annual decreasing rate was 3.7%.The variation of CO2 emissions from energy consumption in agriculture mainly resulted from the increase of agricultural output,improvement of energy utilization efficiency,high carbonization in agricultural energy consumption structure and so forth.Therefore,in order to reduce CO2 emissions from energy consumption in agriculture,it is necessary to vigorously develop rural renewable energy,develop and popularize advanced technology for energy utilization,advance the energy conservation of agricultural machines,establish and improve the macroeconomic control mechanism for carbon emissions from the energy consumption in agricultural production in the further.[Conclusion] The study could provide references for the establishment of policy about reducing carbon emissions from agricultural energy consumption in Guangdong Province.

Optimization of energy consumption structure based on carbon emission reduction target:A case study in Shandong Province,China

It is urgent to significantly reduce greenhouse gas emissions to actively deal with global warming.This paper investigates Shandong Province,a typical province of energy consumption,as the research object,aiming to optimize total energy consumption and consumption structure in the future planning year.This paper constructs a methodological system to optimize energy consumption structure in Shandong Province,using a scenario combination of system dynamics(SD)prediction and analysis based on the coupling of key scenario elements affecting different energy consumption from different perspectives.Structural equation modeling and SD sensitivity analysis indicate an overlap between key factors restricting energy consumption.Pairing the key scenario factors can better reflect the internal mechanism of energy consumption development.Based on this,21 scenarios based on different combinations of the key elements are constructed.Through SD prediction and analysis,the most suitable scenario mode for optimizing energy consumption structure in Shandong Province is selected.This paper provides a suitable development range for the average gross domestic product growth rate,the proportion of secondary industry,energy consumption intensity of secondary industry,and the urbanization rate for Shandong Province.This paper can provide a reference for similar research and the government in formulating the optimization scheme of energy consumption structure.

Analysis on the Change Characteristics of the Correlation between Land Use Structure and Energy Consumption and Carbon Emissions in Kunming from 1997 to 2017

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.

Energy consumption and emission mitigation prediction based on data center traffic and PUE for global data centers

With the rapid development of technologies such as big data and cloud computing,data communication and data computing in the form of exponential growth have led to a large amount of energy consumption in data centers.Globally,data centers will become the world’s largest users of energy consumption,with the ratio rising from 3%in 2017 to 4.5%in 2025.Due to its unique climate and energy-saving advantages,the high-latitude area in the Pan-Arctic region has gradually become a hotspot for data center site selection in recent years.In order to predict and analyze the future energy consumption and carbon emissions of global data centers,this paper presents a new method based on global data center traffic and power usage effectiveness(PUE)for energy consumption prediction.Firstly,global data center traffic growth is predicted based on the Cisco’s research.Secondly,the dynamic global average PUE and the high latitude PUE based on Romonet simulation model are obtained,and then global data center energy consumption with two different scenarios,the decentralized scenario and the centralized scenario,is analyzed quantitatively via the polynomial fitting method.The simulation results show that,in 2030,the global data center energy consumption and carbon emissions are reduced by about 301 billion kWh and 720 million tons CO2 in the centralized scenario compared with that of the decentralized scenario,which confirms that the establishment of data centers in the Pan-Arctic region in the future can effectively relief the climate change and energy problems.This study provides support for global energy consumption prediction,and guidance for the layout of future global data centers from the perspective of energy consumption.Moreover,it provides support of the feasibility of the integration of energy and information networks under the Global Energy Interconnection conception.

Estimate of China's energy carbon emissions peak and analysis on electric power carbon emissions

China＇s energy carbon emissions are projected to peak in 2030 with approximately 110% of its 2020 level under the following conditions： 1） China＇s gross primary energy consumption is 5 Gtce in 2020 and 6 Gtce in 2030; 2） coal＇s share of the energy consumption is 61% in 2020 and 55% in 2030; 3） non-fossil energy＇s share increases from 15% in 2020 to 20% in 2030; 4） through 2030, China＇s GDP grows at an average annual rate of 6%; 5） the annual energy consumption elasticity coefficient is 0.30 in average; and 6） the annual growth rate of energy consumption steadily reduces to within 1%. China＇s electricity generating capacity would be 1,990 GW, with 8,600 TW h of power generation output in 2020. Of that output 66% would be from coal, 5% from gas, and 29% from non-fossil energy. By 2030, electricity generating capacity would reach 3,170 GW with 11,900 TW h of power generation output. Of that output, 56% would be from coal, 6% from gas, and 37% from non-fossil energy. From 2020 to 2030, CO2 emissions from electric power would relatively fall by 0.2 Gt due to lower coal consumption, and rela- tively fall by nearly 0.3 Gt with the installation of more coal-fired cogeneration units. During 2020--2030, the portion of carbon emissions from electric power in China＇s energy consumption is projected to increase by 3.4 percentage points. Although the carbon emissions from electric power would keep increasing to 118% of the 2020 level in 2030, the electric power industry would continue to play a decisive role in achieving the goal of increase in non-fossil energy use. This study proposes countermeasures and recommendations to control carbon emissions peak, including energy system optimization, green-coal-fired electricity generation, and demand side management.

Spatial and temporal variation of energy carbon emissions in Yantai from 2001 to 2011

In order to understand the characteristics of spatial and temporal variation,as well as provide effective ideas on carbon emissions and regulatory policy in Yantai,this article analyzed spatial and temporal variation of carbon emissions in Yantai based on energy consumption statistics for a variety of energy sorts together with industrial sectors from 2001 to 2011.The results were as following:First of all,Yantai's carbon emissions grew by an average of 5.5%per year during the last 10 years,and there was a peak of 10.48 million carbon in the year of 2011.Second,compared with the gross domestic product(GDP) growth rate,the figures for energy carbon emissions growth rate were smaller;however the problem of carbon emissions were still more obvious.Furthermore,carbon emissions in Yantai increased rapidly before 2008;while after 2008,it increased more slowly and gradually become stable.Third,the energy consumption was different among regions in Yantai.For instance,the energy consumption in Longkou city was the largest,which occupied 50%of the total carbon emissions in Yantai;and the energy consumption in Chang Island was generally less than 1%of the Longkou consumption.Finally,there were relative close relationships among the spatial difference of carbon emissions,regional resources endowment,economic development,industrial structure,and energy efficiency.

Forecast of natural gas supply and demand in China under the background of “Dual Carbon Targets”

As a kind of clean energy which creates little carbon dioxide, natural gas will play a key role in the process of achieving “Peak Carbon Dioxide Emission” and “Carbon Neutrality”. The Long-range Energy Alternatives Planning System(LEAP) model was improved by using new parameters including comprehensive energy efficiency and terminal effective energy consumption. The Back Propagation(BP) Neural Network–LEAP model was proposed to predict key data such as total primary energy consumption, energy mix, carbon emissions from energy consumption, and natural gas consumption in China. Moreover, natural gas production in China was forecasted by the production composition method. Finally, based on the forecast results of natural gas supply and demand, suggestions were put forward on the development of China’s natural gas industry under the background of “Dual Carbon Targets”. The research results indicate that under the background of carbon peak and carbon neutrality, China’s primary energy consumption will peak(59.4×10^(8)tce) around 2035, carbon emissions from energy consumption will peak(103.4×10^(8)t) by 2025, and natural gas consumption will peak(6100×10^(8)m^(3)) around 2040, of which the largest increase will be contributed by the power sector and industrial sector. China’s peak natural gas production is about(2800–3400)×10^(8)m^(3), including(2100–2300)×10^(8)m^(3)conventional gas(including tight gas),(600–1050)×10^(8)m^(3)shale gas, and(150–220)×10^(8)m^(3)coalbed methane. Under the background of carbon peak and carbon neutrality, the natural gas consumption and production of China will further increase, showing a great potential of the natural gas industry.

CASE STUDY OF THE OPERATIONAL ENERGY CONSUMPTION AND CARBON EMISSIONS FROM A BUILDING IN NANJING BASED ON A SYSTEM DYNAMICS APPROACH

Buildings are responsible for more than forty percent of global energy consump-tion and as much as one third of global greenhouse gas emissions.Meanwhile,the energy conservation and exhaust reduction of a building can be easily understood by accurately calculating a building’s carbon emissions during its operational stage.In the present study,a system dynamics(SD)approach to calculate the energy consumption and carbon emissions from a building during its operational stage is quantitatively developed through a case study on an office building in Nanjing.The obtained results demonstrate that:a)the difference between the results of SD and that of EnergyPlus is so small that a SD approach is acceptable;b)the variation between the real monitored data and that of simulation by SD and EnergyPlus is reasonable;c)the physical meanings of the variables in the SD model are clear;d)the parameters of the SD model and the relationships between the variables can be determined by a qualitative-and-quantitative combined analysis.

The Prediction of Carbon Emissions Demands in India under the Balance Economic Growth Path

Facing the challenge of climate change, forecasts of energy demand and carbon emissions demand are a key requirement for India to ensure energy security and the balance economic growth. The authors calculate the optimal economic growth under the balance economic growth path from 2009 to 2050 in India based on the economy-carbon dynamic model. Combination of Intergovernmental Panel on Climate Change (IPCC) 2006 edition of the formula of carbon emissions, energy intensity model, and population model, it gets the carbon emissions demand caused by energy consumption for time span 1980-2008. Then, it estimates the energy consumption demand and carbon emissions demand under the balance economic growth path from 2009 to 2050. The results show that the cumulative amount of energy demand and carbon emissions demand in India for the time span 2009 to 2050, are 44.65 Gtoe and 36.16 Gt C, separately. The annual demand of energy consumption and carbon emissions for India show an inverted U curve from 2009 to 2050. The demand of energy consumption and carbon emissions will peak in 2045, and the peak values are 1290.74 Mtoe and 1045.98 Mt C. Furthermore, India’s per capita energy consumption demand and carbon emissions demand also appear maximum values, which are separately 0.81 toe and 0.65 t C.

Mapping urban carbon emissions in relation to local climate zones:Case of the building sector in Bangkok Metropolitan Administration,Thailand

This study focuses on carbon emissions of the building sector in relation to local climate zone(LCZ)classification,concentrating on two major parts.First,we estimated carbon emissions in the building sector,which were cal-culated for weekdays and weekends real-time daily energy consumption patterns.The estimations were divided into direct(from petroleum products consumption)and indirect emissions(from electricity consumption).Sec-ond,we examined urban carbon emissions mapping in relation to LCZ.Bangkok Metropolitan Administration(BMA)was used as the case study and 2016 as the base year for examination.The results illustrate that indirect emissions in Bangkok can be up to ten times higher than direct emissions.The analysis indicates that LCZ,such as compact high-rise,large low-rise,light industry,and warehouse zones had a relatively higher carbon emission intensity than others.Additionally,we identified that the compact high-rise zone has the highest indirect emission intensity,while the light industry and warehouse zone have the greatest direct emission intensity.These results provide insights into the dynamics of carbon emission characteristics in the building sector and the methodology purported here can be used to support low carbon city planning and policymaking in Bangkok.

An Eco-Friendly Approach for Reducing Carbon Emissions in Cloud Data Centers

Based on the Saudi Green initiative,which aims to improve the Kingdom’s environmental status and reduce the carbon emission of more than 278 million tons by 2030 along with a promising plan to achieve netzero carbon by 2060,NEOM city has been proposed to be the“Saudi hub”for green energy,since NEOM is estimated to generate up to 120 Gigawatts(GW)of renewable energy by 2030.Nevertheless,the Information and Communication Technology(ICT)sector is considered a key contributor to global energy consumption and carbon emissions.The data centers are estimated to consume about 13%of the overall global electricity demand by 2030.Thus,reducing the total carbon emissions of the ICT sector plays a vital factor in achieving the Saudi plan to minimize global carbon emissions.Therefore,this paper aims to propose an eco-friendly approach using a Mixed-Integer Linear Programming(MILP)model to reduce the carbon emissions associated with ICT infrastructure in Saudi Arabia.This approach considers the Saudi National Fiber Network(SNFN)as the backbone of Saudi Internet infrastructure.First,we compare two different scenarios of data center locations.The first scenario considers a traditional cloud data center located in Jeddah and Riyadh,whereas the second scenario considers NEOM as a potential cloud data center new location to take advantage of its green energy infrastructure.Then,we calculate the energy consumption and carbon emissions of cloud data centers and their associated energy costs.After that,we optimize the energy efficiency of different cloud data centers’locations(in the SNFN)to reduce the associated carbon emissions and energy costs.Simulation results show that the proposed approach can save up to 94%of the carbon emissions and 62%of the energy cost compared to the current cloud physical topology.These savings are achieved due to the shifting of cloud data centers from cities that have conventional energy sources to a city that has rich in renewable energy sources.Finally,we design a heuristic algorithm to verify the proposed approach,and it gives equivalent results to the MILP model.

中国水泥行业2011-2022年二氧化碳和大气污染物排放分析

2011—2021年,熟料产量呈波动上升趋势。水泥行业整体生产运行水平不断提高,熟料单条生产线平均规模由43.8万t/条提升至115.3万t/条,熟料单位产品综合能耗下降14.4%,熟料单位产品CO_(2)排放强度下降6.3%,但CO_(2)排放总量增加了13.8%,与氮氧化物减排趋势形成较大反差,碳污治理水平差距明显。熟料生产中石灰石分解和煤炭燃烧过程的CO_(2)排放合计占比为92.9%~93.8%,是CO_(2)排放的主要来源。由于熟料系数偏高、非碳酸盐原料替代不足、综合能耗仍然较高等原因,安徽等7个熟料产量大的省份的CO_(2)排放强度高于全国。建议实行碳酸盐熟料产量总量控制,逐步降低熟料应用比例,加快建材市场熟料产品和非碳酸盐原料替代,降低高标号水泥使用比例。应大力推广水泥行业节能降耗增效技术,加快熟料落后产能淘汰。对熟料产量大、碳排放强度高的地区,应结合当地碳排放特点,实行差别化降碳策略。各大气污染防治重点区域应因地施策推进水泥行业减污降碳工作。

保供降碳目标下能源转型模式及转型时点分析

应对气候变化目标要求全球能源系统进行广泛而深刻的转型。由于资源禀赋、能源战略、技术水平等差异,各国能源转型进程呈现出明显的异质性特征。通过对主要国家能源转型特征与减煤路径的分析,归纳了4种典型模式:能效提高模式、增气减煤模式、煤炭洁净利用模式、新能源替代模式,不同模式在不同时间点上与经济社会发展交互影响形成了国际能源转型的内在驱动机制。采用面板数据模型识别了影响能源转型模式的重要因素,并利用历史趋势分析方法对我国化石能源消费“拐点”与减煤时间节点进行研究。研究表明,当人均GDP达到25500美元时,发达国家即出现化石能源减少拐点。国家创新指数和城镇化与煤炭消费占比呈负相关,第二产业增加值占比与煤炭消费占比呈正相关。我国预计减煤时间范围在2028-2035年,化石能源消费减少时点在2040年左右。考虑到我国以煤炭为主的能源资源禀赋,我国能源转型目标的实现须依赖于煤炭洁净利用为主的转型路径,大力发展低碳化现代煤基能源体系,改变传统煤炭利用理念与方式,为未来多能耦合的新型能源体系提供重要支撑。

中国全球价值链地位、环境规制与碳排放绩效

努力实现全球价值链地位攀升与低碳经济发展双目标是中国面临的关键问题。基于2000-2014年中国制造业行业数据,考察全球价值链地位攀升对碳排放绩效的影响与作用机制,可发现:全球价值链地位攀升有助于碳排放绩效改善,其赋能效应在不同类型、技术水平以及污染密集度的行业中存在差异性;能源利用效率与能源消费结构是全球价值链地位作用于碳排放绩效的主要路径,同时环境规制强度的提高可以进一步增强其对碳排放绩效改善的正向作用。我国制造业应全方位、多层次融入全球价值链体系,深化链条间行业的上下游合作,推动制造业内部的能源技术溢出效应转换,对不同行业进行差异化政策支持,同时地方政府也应加强适时监督,规制行业的短视行为。

能耗双控转向碳排放双控的理论逻辑与实践路径

能耗双控向碳排放双控转变是推进碳达峰碳中和的制度保障,系统分析能耗双控转向碳排放双控的理论逻辑及实践路径,对促进企业适应国内国际市场、降低碳排放,保障中国“双碳”目标的实现有重要现实意义。能耗双控通过需求效应、技术效应和要素替代效应影响节能降碳,碳排放双控主要通过技术创新效应、技术溢出效应和产业结构升级效应的综合作用促进节能降碳,碳排放双控节能降碳的作用机制更加完善,更加注重碳排放控制,更能够精准控碳。能耗双控转向碳排放双控有利于企业能源转型、绿电消费的增加,对于“双碳”目标的实现、国际竞争力的提高具有深远的影响。能耗双控转向碳排放双控背景下,中国面临能耗双控转向碳排放双控的识别阶段、分阶段、落实阶段难题,碳市场建设不足,碳排放监管和执法体系不完善的挑战;为顺利推动能耗双控转向碳排放双控,本文提出统筹推进分阶段、差异化考核工作,通过加大碳市场覆盖范围、完善碳交易机制、优化用能权交易市场等加强碳市场建设,加强碳排放监管和法治体系建设等关键举措和推动路径。中国实现绿色低碳发展须注重能耗双控向碳排放双控转变,准确把握能耗双控转向碳排放双控,促进“双碳”目标实现。

中国煤炭价格与碳排放权交易价格的传导路径研究

碳市场作为新兴市场易受能源价格因素干扰,在“双碳”目标下研究煤炭价格与碳排放权交易价格(以下简称为碳价)的传导路径,有利于碳市场的健康运行并助力其发挥减排作用。在梳理煤炭价格与碳价传导机理的基础上,利用DCC-GARCH模型揭示煤炭指数与碳价的动态关系,并运用VAR模型实证检验煤炭价格与碳价的传导路径及能源消费结构、能源效率与动态相关系数的因果关系。实证结果表明:煤炭价格与碳价存在显著动态相关性,并呈现复杂性和多变性;碳市场尚未充分发挥其创新激励效应,相较于能源利用效率,煤炭价格与碳价通过能源消费结构路径实现的传导更加显著;能源消费结构调整是动态相关系数变化的格兰杰原因,而能源利用效率改变则不是。

碳排放权交易能提升高能耗企业韧性吗?——基于准自然实验的证据

当前全球经济模糊性、复杂性和不稳定性突出,企业韧性成为评价企业可持续发展能力的新指标,而碳排放权交易政策是我国实现“碳达峰、碳中和”目标的重要抓手。基于中国2009—2022年八大高耗能行业上市企业数据,运用多时点双重差分模型探究碳排放权交易政策对高能耗企业韧性的影响及作用机制。研究发现,碳排放权交易政策有助于提升高能耗企业韧性,主要通过发挥创新效应、信息效应和绿色治理效应提升高能耗企业韧性水平。内部控制水平和政府补助在这一关系中发挥正向调节作用,融资约束发挥抑制作用。异质性分析表明,在非国有、大规模以及高科技企业中,碳排放权交易对高能耗企业韧性的提升作用更显著。因此,研究碳排放权交易政策对高能耗企业韧性这一微观层面的经济后果,对我国实现“碳达峰、碳中和”目标、保持企业持续稳定健康发展具有重要意义。

某酒店制冷机房运行策略优化

对华东地区某酒店制冷机房运行策略进行了优化,利用能耗模拟软件对多种运行方案进行了仿真分析。结果表明:原方案仅对冷水泵采用变频控制,制冷机房能效比为4.62;优化方案1采取传统变频方式,对冷水泵、冷却水泵、冷却塔风机采用变频控制,制冷机房能效比达到5.32,与原方案相比提升了15.1%;优化方案2通过对冷水机组、冷水泵、冷却水泵、冷却塔风机进行优化控制,制冷机房能效比为5.48,与原方案相比能效比提升了18.6%,且每年可减少约79 t碳排放量,节能效果显著。