Understanding and Attribution of Extreme Heat and Drought Events in 2022: Current Situation and Future Challenges

Extreme weather events and their consequential impacts have been a key feature of the climate in recent years in many parts of the world,with many partly attributed to ongoing global-scale warming.The past year,2022,has been no exception,with further records being broken.The year was marked by unprecedented heatwaves and droughts with highly unusual spatial extent,duration and intensity,with one measure indicating an aggregated and overall intensity of extreme heat events worldwide not seen since at least 1950.The extreme drought measured by surface soil moisture covered 47.3%of global land areas in 2022,which was the second most widespread year since 1980.Here,we examine notable events of the year in five major regions of the world:China’s Yangtze River region,western Europe,the western U.S.,the Horn of Africa and central South America.For each event,we review the potential roles of circulation,oceanic forcing(especially the“triple-dip”La Niña)and anthropogenic climate change,with an aim of understanding the extreme events in 2022 from a global perspective.This will serve as a reference for mechanism understanding,prediction and attribution of extreme events.

On the Attribution of Mercury’s Perihelion Precession

Although Newtonian gravity and general relativity predicted the precession of Mercury perihelion historically, many improved methods continue to predict the precession of Mercury during recent decades of years. Uncertainties in various predictions and observations suggest that the attribution of Mercury’s precession is still not well understood. This paper argues that the cause of Mercury’s precession is not gravity, but the inertia of material motion left over from the formation of the solar system. According to this inertia theory, the planetary precession is associated with the ratio of total mass-energy density of the system to the mass-energy of the Sun and its change over time. If other factors are not changed with time, the perihelion precession of planets per orbit is proportional to his distance relative to the Sun. The conclusions of this paper can provide more effective factor considerations for the complete description of various astronomical events and phenomena using general relativity equations.

A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

Not in Control,but Liable?Attributing Human Responsibility for Fully Automated Vehicle Accidents

Human agency has become increasingly limited in complex systems with increasingly automated decision-making capabilities.For instance,human occupants are passengers and do not have direct vehicle control in fully automated cars(i.e.,driverless cars).An interesting question is whether users are responsible for the accidents of these cars.Normative ethical and legal analyses frequently argue that individuals should not bear responsibility for harm beyond their control.Here,we consider human judgment of responsibility for accidents involving fully automated cars through three studies with seven experiments(N=2668).We compared the responsibility attributed to the occupants in three conditions:an owner in his private fully automated car,a passenger in a driverless robotaxi,and a passenger in a conventional taxi,where none of these three occupants have direct vehicle control over the involved vehicles that cause identical pedestrian injury.In contrast to normative analyses,we show that the occupants of driverless cars(private cars and robotaxis)are attributed more responsibility than conventional taxi passengers.This dilemma is robust across different contexts(e.g.,participants from China vs the Republic of Korea,participants with first-vs third-person perspectives,and occupant presence vs absence).Furthermore,we observe that this is not due to the perception that these occupants have greater control over driving but because they are more expected to foresee the potential consequences of using driverless cars.Our findings suggest that when driverless vehicles(private cars and taxis)cause harm,their users may face more social pressure,which public discourse and legal regulations should manage appropriately.

Attribute Reduction Method Based on Sequential Three-Branch Decision Model

Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundant attribute calculations, high time consumption, and low reduction efficiency. In this paper, based on the idea of sequential three-branch decision classification domain, attributes are treated as objects of three-branch division, and attributes are divided into core attributes, relatively necessary attributes, and unnecessary attributes using attribute importance and thresholds. Core attributes are added to the decision attribute set, unnecessary attributes are rejected from being added, and relatively necessary attributes are repeatedly divided until the reduction result is obtained. Experiments were conducted on 8 groups of UCI datasets, and the results show that, compared to traditional reduction methods, the method proposed in this paper can effectively reduce time consumption while ensuring classification performance.

Machine learning for carbonate formation drilling: Mud loss prediction using seismic attributes and mud loss records

Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.

Aboveground biomass stocks of species-rich natural forests in southern China are influenced by stand structural attributes,species richness and precipitation

Forests,the largest terrestrial carbon sinks,play an important role in carbon sequestration and climate change mitigation.Although forest attributes and environmental factors have been shown to impact aboveground biomass,their influence on biomass stocks in species-rich forests in southern China,a biodiversity hotspot,has rarely been investigated.In this study,we characterized the effects of environmental factors,forest structure,and species diversity on aboveground biomass stocks of 30 plots(1 ha each) in natural forests located within seven nature reserves distributed across subtropical and marginal tropical zones in Guangxi,China.Our results indicate that forest aboveground biomass stocks in this region are lower than those in mature tropical and subtropical forests in other regions.Furthermore,we found that aboveground biomass was positively correlated with stand age,mean annual precipitation,elevation,structural attributes and species richness,although not with species evenness.When we compared stands with the same basal area,we found that aboveground biomass stock was higher in communities with a higher coefficient of variation of diameter at breast height.These findings highlight the importance of maintaining forest structural diversity and species richness to promote aboveground biomass accumulation and reveal the potential impacts of precipitation changes resulting from climate warming on the ecosystem services of subtropical and northern tropical forests in China.Notably,many natural forests in southern China are not fully stocked.Therefore,their continued growth will increase their carbon storage over time.

A risk assessment method considering risk attributes and work safety informational needs and its application

The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evolve to address the existing and future challenges by considering the new demands and advancements in safety management.The study aims to propose a systematic and comprehensive risk assessment method to meet the needs of process system safety management.The methodology first incorporates possibility,severity,and dynamicity(PSD)to structure the“51X”evaluation indicator system,including the inherent,management,and disturbance risk factors.Subsequently,the four-tier(risk point-unit-enterprise-region)risk assessment(RA)mathematical model has been established to consider supervision needs.And in conclusion,the application of the PSD-RA method in ammonia refrigeration workshop cases and safety risk monitoring systems is presented to illustrate the feasibility and effectiveness of the proposed PSD-RA method in safety management.The findings show that the PSD-RA method can be well integrated with the needs of safety work informatization,which is also helpful for implementing the enterprise's safety work responsibility and the government's safety supervision responsibility.

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

A Study on Multivariable Interactions Concerning Radar Cross Section Reduction through Geometric Attributes

This resolution 5 (25−1 factorial) study aimed to ascertain an understanding of the interactions between different geometries on the resulting Radar Cross Section (RCS) of a target. The results of the study are in line with the general understanding of the impact different geometries have on RCS but show that geometries can also influence the variance of measured RCS, and typical attributes that reduce RCS increase the variance of the measured RCS. Notably, an increased angle between the front face of a plate and the direction of the radar signal decreased RCS but increased the variance of the RCS measured.

Attributes of Domestic Spaces for Contemporary Habitation-A Secondary Publication

The domestic space can be defined as the sphere that articulates the needs for subjective containment and contextual stimuli.In this sense,questions arise about the indispensable attributes that spaces must possess for this articulation to take place adequately.Architecture,as the discipline in charge of satisfying the specific spatial needs of those who inhabit these spaces and,in a broader sense,as a concrete contribution to society,must address this relationship in all its complexity and generate concrete responses that incorporate the appropriate spatial attributes during the design processes.The design processes that shape living spaces confront this dialectic,and the manner in which they do so brings identity and character to them.It is believed that the higher the level of variables that are contemplated and weighted,the greater the adequacy of spaces to the changing dynamics of the people who inhabit them.This article focuses on a thorough analysis of these spatial attributes,in parallel to the definition of each one as a particular condition for design,based on their conceptualization,breakdown,and articulation.Conceptually,the following attributes are addressed:flexibility,adaptability,variability,versatility,multiplicity,plurality,integrality,gradualness,incrementality,progressiveness,independence,connectivity,intimacy,and privacy.Each of these attributes is valued as a contribution to creating adequate habitability in contextual terms,with consideration to possible integrations and combinations.

Attribute Reduction of Neighborhood Rough Set Based on Discernment

For neighborhood rough set attribute reduction algorithms based on dependency degree,a neighborhood computation method incorporating attribute weight values and a neighborhood rough set attribute reduction algorithm using discernment as the heuristic information was proposed.The reduction algorithm comprehensively considers the dependency degree and neighborhood granulation degree of attributes,allowing for a more accurate measurement of the importance degrees of attributes.Example analyses and experimental results demonstrate the feasibility and effectiveness of the algorithm.

天津市工业部门碳排放强度研究:基于LMDI-Attribution分析方法

引入对数平均迪式分解模型及其归因分析(LMDI-Attribution)方法,从细分行业角度对2000-2012年天津市工业部门的碳排放强度变化进行研究,首先对碳排放强度作产业结构、能源强度和排放因子三因素LMDI乘法分解,其后,基于三个分解因素在2000-2006年和2006-2012年两个时间段对碳排放强度下降的影响效应,对其作归因分析,量化36个细分行业对分解因素影响效应的贡献。得到以下主要结论:天津市工业部门的碳排放强度从2000-2012年累计下降了66.87%,产业结构和能源强度是其下降的主导因素,累计影响值分别为-44.53%和-47.39%,排放因子对其下降起抑制作用,累计影响值为15.39%;产业结构对碳排放强度的影响主要依赖于高耗能行业产值占比的变化,化工行业和黑色金属行业产值的占比越高,产业结构的减排效应越差,因此在调整产业结构时,应大力发展服务业和高新技术产业,严格控制高耗能行业发展;黑色金属行业和非金属矿物制品业是能源强度效应变化的主导行业,由于技术进步和能源效率的提高,黑色金属行业的负值贡献在2006-2012年相对前七年增大了2.56倍,非金属矿物制品业从最大的正值贡献行业转为第二大负值贡献行业,使得2006-2012年能源强度对碳排放强度的拉低影响相对前七年增大了近1倍;排放因子对碳排放强度下降的抑制作用在2006年之后呈现减弱的趋势,主要是因为"十一五"期间化工行业能源结构优化与能源再利用体系的建立,有效抑制了排放因子对碳排放强度的拉升作用。综上可见,黑色金属行业、化工行业和非金属矿物制品业是天津市碳排放强度分解因素变化的主要贡献行业,以这些高耗能行业为主要研究对象,根据行业特点及其所处的发展阶段,严格控制其粗放型发展、鼓励新技术研发、循环利用资源及优化用能结构等措施,针对性的制定节能减排办法,才能在保持经济快速发展的同时做到碳排放强度的降低。

基于LMDI-Attribution的江苏省水足迹效率变化驱动力分析

在长江经济带各省市中,江苏省水足迹总量和人均量最大,缺水程度最严重,较低的水资源可持续发展能力制约了江苏省经济社会的发展。从水足迹视角,聚焦江苏省水足迹效率变化,分析水足迹效率变化的驱动因素和大小,为江苏省水资源可持续发展提出建议。基于2010—2019年江苏省水足迹计算结果,扩展Kaya恒等式构造出驱动水足迹效率变化的实际用水效率、水资源利用技术水平和水足迹结构3个因素,借助LMDI-Attribution模型计算各因素驱动力大小,并得到三大用水部门的具体贡献度。结果表明:研究期间江苏省水足迹效率增长幅度较大,从2010年的32.78元/m~3增加到2019年的88.91元/m~3,增幅为171.25%,其中实际用水效率为水足迹效率变化的最大驱动力,贡献了167%,其次为技术水平,贡献了26.15%;由于农业部门水足迹占比增大,水足迹结构对变化的影响为-21.67%;从用水部门来看,工业部门和生活部门的实际用水效率是最关键的驱动力,分别为91.19%和75.07%,农业部门仅为0.73%;技术水平驱动力中贡献最大的为生活部门的21.40%,其次为工业部门的4.79%,而农业部门为-0.04%;水足迹结构驱动力中仅农业部门是正向的,为0.06%,生活部门为-2.13%,工业部门为-19.60%。建议江苏省建立健全节水型农业体系,提高农业用水效率,降低农业部门水足迹所占比例,保持各用水部门灰水足迹的下降趋势,限制高污染企业的投资建设,支持绿色企业的发展;刺激省内消费需求,不断优化对外贸易结构,提升水资源可持续发展能力。

Distribution of high-risk human papillomavirus genotype prevalence and attribution to cervical precancerous lesions in rural North China

Objective: Precise prevention is more desired for cervical cancer due to the huge population, high prevalence of human papillomavirus(HPV) infection in China and the vision of screen-and-treat strategies in low-and middleincome countries(LMICs). Considerations of combining type-specific prevalence and attribution proportion to high-grade cervical intraepithelial neoplasia are informative to more precise and effective region-specific cervical cancer prevention and control programs. The aim of the current study was to determine the genotype distribution of HPV and attribution to cervical precancerous lesions among women from rural areas in North China.Methods: A total of 9,526 women participated in the cervical cancer screening project in rural China. The samples of women who tested positive for HPV were retested with a polymerase chain reaction(PCR)-based HPV genotyping test. The attribution proportion of specific high-risk human papillomavirus(HR-HPV) types for different grades of cervical lesions was calculated by using the type contribution weighting method.Results: A total of 22.2%(2,112/9,526) of women were HR-HPV positive and HPV52(21.7%) was the most common HR-HPV genotype, followed by HPV58(18.2%), HPV53(18.2%) and HPV16(16.2%). The top three genotypes detected in HR-HPV-positive cervical intraepithelial neoplasia(CIN)1 were HPV16(36.7%), HPV58(20.4%), HPV56(15.3%). Among CIN2+, the most frequent genotypes were HPV16(75.6%), HPV52(17.8%),HPV58(16.7%). HPV16, 56, 58, 53, 52, 59, 68, and 18 combined were attributed to 84.17% of all CIN1 lesions,and HPV16, 58, and 52 combined were attributed to 86.98% of all CIN2+ lesions.Conclusions: The prevalence of HR-HPV infection among women from rural areas in North China was high and HPV16, HPV58, HPV52 had paramount attributable fraction in CIN2+. Type-specific HPV prevalence and attribution proportion to cervical precancerous lesions should be taken into consideration in the development of vaccines and strategy for screening in this population.

Attribution retraining group therapy for outpatients with major depression disorder,generalized anxiety disorder,and obsessive-compulsive disorder:a pilot study

The aim of this present study is to examine the efficacy of attribution retraining group therapy （ARGT） and to compare the responses of outpatients with major depression disorder （MDD）, generalized anxiety disorder （GAD） and obsessive-compulsive disorder （OCD）. We carried out a prospective uncontrolled intervention study with a 8-weeks of ARGT on sixty three outpatients with MDD, GAD or OCD. Hamilton rating scale for depression, Hamilton rating scale for anxiety, Yale-Brown obsessive-compulsive scale, attribution style questionnaire, self-esteem scale, index of well-being, and social disability screening schedule were administered before and after treatment. Significant improvement in symptoms and psychological and social functions from pre- to posttreatment occurred for all participants. The changes favored MDD patients. Our study suggested that ARGT may improve the symptoms and psychological-social functions of MDD, GAD, and OCD patients. MDD patients showed the best response.

Attribution of the Langshan Tectonic Belt: Evidence from zircon U-Pb ages and Hf isotope compositions

This study describes a previously unidentified Neoproterozoic mafic dyke emplaced in the northern flank of the Langshan Tectonic Belt. This dyke intruded into the micaquartz schist of the Zhaertaishan Group, and yielded an age of 908 ± 8 Ma. The youngest U-Pb ages of micaquartz schist from the Zhaertaishan Group in the Langshan area were 1118 ± 33 Ma,1187 ± 3 Ma and 1189 ± 39 Ma,suggesting that the depositional age of the protolith of the schist was between 908 ± 8 Ma and 1118 ± 33 Ma. In addition, 436 U-Pb age data and 155 Lu-Hf isotopic data from six samples in the Langshan Tectonic Belt and one Permian greywacke from the Wuhai area show distinct differences between the northern and southern flanks of the Main Langshan area. The U-Pb ages of the northern flank are primarily Meso-Neoproterozoic; similar ages have not been identified in the southern flank to date.Moreover, two-stage Hf model ages of the northern flank feature three age peaks at ～900 Ma,～1700 Ma and ～2600 Ma; this differs from Hf model ages of the southern flank, which feature one strong age peak at ～2700 Ma. These results suggest that the northern and southern flanks of the Main Langshan area have different geochronologic characteristics and should be divided further. Based on the U-Pb ages and Hf model ages, the northern and southern flanks of the Main Langshan area are named the North and South Langshan Tectonic Belts. Comparison of the U-Pb age and two-stage Hf model age distributions from the North Langshan Tectonic Belt, South Langshan Tectonic Belt, Alxa Block and the North China Craton(NCC) reveal that the North Langshan Tectonic Belt is similar to the Alxa Block and that the South Langshan Tectonic Belt is similar to the NCC. In addition, the zircon U-Pb age of 860 ±7 Ma commonly observed in the Alxa Block was detected in the Permian greywacke from the Wuhai area of the NCC, which suggests that the amalgamation of the North and South Langshan Tectonic belts(i.e.,the amalgamation of the Alxa Block and the NCC), occurred between Devonian and late Permian.

Documented changes in annual runoff and attribution since the 1950s within selected rivers in China

To enable local water resource management and maintenance of ecosystem integrity and to protect and mitigate against flood and drought, it is necessary to determine changes in long-term series of streamflow and to distinguish the roles that climate change and human disturbance play in these changes. A review of previous research on the detection and attribution of observed changes in annual runoff in China shows a decrease in annual runoff since the 1950s in northern China in areas such as the Songhuajiang River water resources zone, the Liaohe River water resources zone, the Haihe River water resources zone, the Yellow River water resources zone, and the Huaihe River water resources Zone. Furthermore, abrupt changes in annual runoff occurred mostly in the 1970s and 1980s in all the above zones, except for some of the sub-basins in the middle Yellow River where abrupt change occurred in the 1990s. Changes in annual runoff are found to be mainly caused by climate change in the western Songhuajiang River basin, the upper mainstream of the Yangtze River, and the western Pearl River basin, which shows that studies on the impact of climate change on future water resources under different climate change scenarios are required to enable planning and management by agencies in these river basins. However, changes in annual runoff were found to be mainly caused by human activities in most of the catchments in northern China (such as the southern Songhuajiang River, Liaohe River, Haihe River, the lower reach and some of the catchments within the middle Yellow River basin) and in middle-eastern China, such as the Huaihe River and lower mainstream of the Yangtze River. This suggests that current hydro-climatic data can continue to be used in water-use planning and that policymakers need to focus on water resource management and protection.

Forest disturbances and the attribution derived from yearly Landsat time series over 1990–2020 in the Hengduan Mountains Region of Southwest China

Background:Natural forests in the Hengduan Mountains Region(HDMR)have pivotal ecological functions and provide diverse ecosystem services.Capturing long-term forest disturbance and drivers at a regional scale is crucial for sustainable forest management and biodiversity conservation.Methods:We used 30-m resolution Landsat time series images and the LandTrendr algorithm on the Google Earth Engine cloud platform to map forest disturbances at an annual time scale between 1990 and 2020 and attributed causal agents of forest disturbance,including fire,logging,road construction and insects,using disturbance properties and spectral and topographic variables in the random forest model.Results:The conventional and area-adjusted overall accuracies(OAs)of the forest disturbance map were 92.3% and 97.70%±0.06%,respectively,and the OA of mapping disturbance agents was 85.80%.The estimated disturbed forest area totalled 3313.13 km^(2)(approximately 2.31% of the total forest area in 1990)from 1990 to 2020,with considerable interannual fluctuations and significant regional differences.The predominant disturbance agent was fire,which comprised approximately 83.33% of the forest area disturbance,followed by logging(12.2%),insects(2.4%)and road construction(2.0%).Massive forest disturbances occurred mainly before 2000,and the post-2000 annual disturbance area significantly dropped by 55% compared with the pre-2000 value.Conclusions:This study provided spatially explicit and retrospective information on annual forest disturbance and associated agents in the HDMR.The findings suggest that China’s logging bans in natural forests combined with other forest sustainability programmes have effectively curbed forest disturbances in the HDMR,which has implications for enhancing future forest management and biodiversity conservation.

基于LMDI-Attribution的山西省碳排放强度分析

运用LMDI-Attribution方法对山西省2005~2014年间碳排放强度进行了分解及归因分析,并将传统的LMDI模型扩展到产业部门和居民生活两部分,量化五大行业对影响效应变化的贡献率。结果表明,山西省碳排放强度呈下降趋势,2014年万元生产值碳排放强度比2005年减少了2.603 29 t。其中,影响其变化的3个主要因素是经济规模、能源强度和居民能源消费结构;农业、工业、建筑业、交通业、商业5个行业对能源强度效应和产业结构效应均有抑制碳排放强度增长作用;其中,工业是能源结构、能源强度和产业结构3个效应变化的最主要因素,而低耗能的农业、交通业、商业的影响力微弱。总体而言,工业是山西省未来减排最需要关注的部门,优化产业结构和能源结构,大力发展清洁能源是山西省绿色低碳发展的最佳途径。