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.

Inter-Provincial Transaction Model in Two-Level ElectricityMarket Considering Carbon Emission and Consumption Responsibility Weights

In the context of the joint operation of China’s intra-provincial markets and inter-provincial trading,how to meet the load demand and energy consumption using inter-provincial renewable energy trading is a key problem.The combined operation of intra-provincial and inter-provincial markets provides a new way for provincial power companies to optimize and clear the intra-provincial power market,complete the intra-provincial consumption responsibility weight index,and consume renewable energy across provinces and regions.This paper combines power generation and consumption within the province,uses inter-provincial renewable energy trading tomeet the load demand within the province and completes the index of intra-provincial consumption responsibility weights.The intra-provincial market trading and inter-provincial market clearing are respectively taken as the upper and lower levels of the model.Under the two-level electricity market operation framework,the upper-level model aims to minimize the expected total operating cost within the province considering the carbon emission cost and the weight of the consumption responsibility,while the lower-level model aims to minimize the inter-provincial renewable energy purchasing cost.Finally,the influence of inter-provincial transaction mechanism,risk aversion coefficient,voucher price,and responsibility weight on operating cost is analyzed.Simulation is used to verify that the proposed model can meet the requirements of the provincial load power consumption and the consumption responsibility weight index,and promote the consumption of renewable energy.

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.

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 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.

Spatial Patterns and Driving Factors of Urban Residential Embedded Carbon Emissions: An Empirical Study in Kaifeng, China

With the continuous improvement in living standards and great changes in lifestyles, more attention is being paid to the embedded carbon emissions produced by human consumption. With large sample data and high-resolution remote sensing images, we explored the spatial differentiation and influencing factors of household embedded carbon emissions within the city fine scale using the EIO-LCA model, spatial autocorrelation analysis and standard deviation ellipse, quantile regression, etc. The results indicate that the spatial dependence is more obvious than the characteristics of spatial heterogeneity;the high-value area of household embedded carbon emissions gathers in new development zones in cities that are expanding rapidly, mainly with residents in large number of newly-built commercial housing families and the relative’s courtyard of institutions. The factors of family characteristics, housing characteristics, lifestyles, and consumption concept have significant effects on the embedded carbon emissions of each person. The influencing intensity of most factors showed an increasing trend with increased carbon emissions. The study verified the impact of urban sprawl on residential carbon emissions and the applicability of the situated lifestyles theory in the construction of urban low-carbon communities in China.

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.

Correlation between carbon emissions,fuel consumption of vehicles and speed limit on expressway

This paper aimed to investigate the correlation between carbon emissions,fuel consumption,and speed limit.A theoretical model was derived based on the energy conservation law,which expresses the relationship between vehicle's fuel consumption and speed.Subsequently,a total of 40 sets of fuel consumption data were collected through field tests to verify the accuracy of the theoretical model at different speeds and different road longitudinal slope combinations.The fuel consumption was then converted to carbon emissions according to the carbon emission factors specified by Intergovernmental Panel on Climate Change(IPCC).In the field experiment,two types of cars and trucks,which are most common on the expressways in China,were selected.Finally,the travel speed under different posted speed limits was obtained through the previously established model,and the carbon emission changes of different vehicle types at different limited speeds are calculated.The results show that the speed limit has a significant impact on fuel consumption and carbon emissions.When the speed limit increased from 80 to 120 km/h,average vehicle speeds increased about 21%-27%,and fuel consumption and carbon emissions increased from approximately 33%-38%.Another interesting result was that the vehicle's fuel consumption and carbon emissions are only affected by speed.The results of the study explore the effect of speed limits on carbon emissions and provide evidence for road managers to set reasonable speed limits.

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.

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.

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.

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.

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.

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.

Estimating the Effect of Carbon Tax on CO₂Emissions of Coal in China

Using the co-integration model and the VAR model, this article estimates the effect of carbon taxes on CO2 emissions of coal in 2020. The estimation for the long-run price elasticity of coal in China is –0.34, which shows more elasticity than those of previous studies. The main reason lies in the fact that none of the previous studies considered the structural breaks of Chinese energy consumption in 2006. The levy of 100RMB, 150RMB and 200RMB on per ton of standard coal from 2012 in China will decrease the consumption of coal by 4.88%, 7.31% and 9.75% respectively in 2020, which will further lead to the decrease of CO2 emissions in 2020 by 8.69%, 13.02% and 17.36% respectively. This observation implies that the use of carbon tax scheme is one of the most practical policies that can mitigate the challenge of climate change. However, the implementation measures should be deliberately designed in such a way that making heavy impact on economic development of China is avoided.

Influencing factors and paths of direct carbon emissions from the energy consumption of rural residents in central China determined using a questionnaire survey

Rural residents have unique lifestyle characteristics,energy consumption methods,energy-saving behaviors,and awareness.And the direct carbon emission from rural residents is based on the combined effect of multiple factors.In order to address the complexity of factors affecting the direct carbon emissions from rural household,this study used a structural equation model to examine the effect of multi-factor variables on direct carbon emissions from rural households in central China.Data were collected using questionnaires and surveys in six cities in central China to reflect the daily reality of rural residents.The results show that quality of life and awareness of energy conservation can affect the direct carbon emissions of rural residents.Family characteristics and awareness of energy conservation affected carbon emissions indirectly by affecting the daily behaviors and quality of life of the residents;consumption characteristics,energy-saving behaviors,and energy conservation policies are not the main factors contributing to the direct carbon emissions of the residents.Based on the results,future studies can focus on energy conservation education,improvement of the living habits,coal energy use efficiency,and energy consumption structure of residences.

Contribution of high-latitude permafrost regions in the Northern Hemisphere to global wildfire carbon emissions

Wildfires are major disturbances in permafrost ecosystems, with increasing frequency and intensity in recent years. In permafrost regions, wildfires not only burn surface and subsurface organic matter but also accelerate permafrost thawing,releasing significant amounts of greenhouse gases such as carbon dioxide and methane into the atmosphere. However, the contribution of high-latitude permafrost regions in the Northern Hemisphere to global wildfire carbon emissions remains poorly understood. This study integrates remote sensing data and ground observations to analyze the contributions of aboveground and belowground fuel combustion in high-latitude permafrost regions to global wildfire carbon emissions from 2002 to 2020, as well as the spatiotemporal variations in these contributions. Our findings indicate that permafrost regions contribute approximately11.96% of global wildfire carbon emissions, with aboveground emissions accounting for approximately 3.94% of global aboveground emissions and belowground emissions contributing approximately 63.57% of global belowground emissions. The contribution of high-latitude permafrost regions to global emissions peaked in July and August, whereas the continuous permafrost zones(areas with more than 90% permafrost coverage) showed the most significant increase in June and July. The contributions of both aboveground and belowground emissions from high-latitude permafrost regions to global wildfire emissions have been increasing, primarily due to the reduction in global wildfire emissions, in contrast with the rising emissions from wildfires in high-latitude permafrost regions. This study highlights the significant role of wildfires, particularly the combustion of belowground biomass in high-latitude permafrost regions, in global carbon emissions. The decomposition and combustion of organic carbon in permafrost regions due to wildfires release more greenhouse gases into the atmosphere, potentially amplifying the positive feedback between atmospheric greenhouse gas accumulation and climate warming.

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.

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.

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。