Spatial-temporal Evolution Characteristics and Decoupling Analysis of Influencing Factors of China’s Aviation Carbon Emissions

The aviation industry has become one of the top ten greenhouse gas emission industries in the world. China’s aviation carbon emissions continue to increase, but the analysis of its influencing factors at the provincial level is still incomplete. This paper firstly uses Stochastic Impacts by Regression on Population, Affluence and Technology model(STIRPAT) model to analyze the time series evolution of China’s aviation carbon emissions from 2000 to 2019. Secondly, it uses the Logarithmic Mean Divisia Index(LDMI) model to analyze the influencing characteristics and degree of four factors on China’s aviation carbon emissions, which are air transportation revenue, aviation route structure, air transportation intensity and aviation energy intensity. Thirdly, it determines the various factors’ influencing direction and evolution trend of 31 provinces’ aviation carbon emissions in China(not including Hong Kong, Macao, Taiwan of China due to incomplete data). Finally, it derives the decoupling effort model and analyzes the decoupling relationship and decoupling effort degree between air carbon emissions and air transportation revenue in different provinces. The study found that from 2000 to2019, China’s total aviation carbon emissions continued to grow, while the growth rate of aviation carbon emissions showed a fluctuating downward trend. Air transportation revenue and aviation route structure promote the growth of total aviation carbon emissions, and air transportation intensity and aviation energy intensity have a restraining effect on the growth of total aviation carbon emissions. The scope of negative driving effect of air transportation revenue and air transportation intensity on total aviation carbon emissions in various provinces has increased. While the scope of positive driving influence of aviation route structure on total aviation carbon emissions of various provinces has increased, aviation energy intensity mainly has negative driving influence on total aviation carbon emissions of each province. Overall, the emission reduction trend in the areas to the west and north of the Qinling-Huaihe River Line is obvious. The decoupling mode between air carbon emissions and air transportation revenue in 31 provinces is mainly expansion negative decoupling.The air transportation intensity effect shows strong decoupling efforts in most provinces, the decoupling effort of aviation route structure effect and aviation energy intensity effect is not prominent.

Carbon Emission Trends of Manufacturing and Influencing Factors in Jilin Province,China

This paper constructed a carbon emission identity based on five factors： industrial activity, industrial structure, energy inten- sity, energy mix and carbon emission parameter, and analyzed manufacturing carbon emission trends in Jilin Province at subdivided industrial level through Log-Mean Divisia Index （LMDI） method. Results showed that manufacturing carbon emissions of Jilin Province increased 1.304 ~ 107t by 66% between 2004 and 2010. However, 2012 was a remarkable year in which carbon emissions decreased compared with 2011, the first fall since 2004. Industrial activity was the most important factor for the increase of carbon emissions, while energy intensity had the greatest impact on inhibiting carbon emission growth. Despite the impact of industrial structure on carbon emissions fluctuated, its overall trend inhibited carbon emission growth. Further, influences of industrial structure became gradually stronger and surpassed energy intensity in the period 2009-2010. These results conclude that reducing energy intensity is still the main way for carbon emission reduction in Jilin Province, hut industrial structure can not be ignored and it has great potential. Based on the analyses, the way of manufacturing industrial structure adjustment for Jilin Province is put forward.

Spatial network structure of transportation carbon emission efficiency in China and its influencing factors

Grasping the spatial correlation structure of transportation carbon emission efficiency(TCEE)and its influencing factors is significant for promoting high-quality and coordinated development of the transportation industry and the relevant region.Based on the ideal point cross-efficiency(IPCE)model,the social network analysis method was employed herein to explore the spatial correlation network structure of China’s provincial TCEE and its influencing factors.The results obtained showed the following outcomes.(1)During the study period,China’s provincial TCEE formed a complex and multithreaded network association relationship,but its network association structure was still relatively loose and presented the hierarchical gradient characteristics of dense in the east and sparse in the west.(2)The correlation of China’s TCEE formed a block segmentation based on the regional boundaries,and its factional structure was relatively obvious.The eastern region was closely connected with the central region,and generally connected with the western and northeastern regions.The central region was mainly connected with the eastern and western regions,and relatively less connected with the northeastern region.Besides,the northeastern region was weakly connected with the western region.(3)Shanghai,Beijing,Zhejiang,Guangdong,Jiangsu,Tianjin,and other developed provinces were in the core leading position in the TCEE network,which significantly impacted the spatial correlation of TCEE.However,Heilongjiang,Jilin,Xinjiang,Qinghai,and other remote provinces in the northeast and northwest were at the absolute edge of the network,which weakly impacted the spatial correlation of TCEE.(4)Provincial distance,economic development-level difference,transportation intensity difference,and transportation structure difference had significant negative impacts on the spatial correlation network of China’s provincial TCEE.In contrast,the energy-saving technology level difference had a significant positive impact on it.The regression coefficients of transportation energy structure and environmental regulation differences were positive but insignificant;their response mechanism and effects need to be improved and enhanced.

Low Carbon Beijing:Research on the Influencing Factors of Carbon Emission Trading Price

The international community has taken extensive actions to achieve carbon neutrality and sustainable development with the intensification of global warming and climate change.China has also carried out a long-term layout,setting the goal of achieving a carbon peak by 2030 and carbon neutrality by 2060.In 2021,with the official launch of a unified national carbon emissions trading market,China’s nationwide carbon emissions trading kicked off.Carbon emission trading is an important policy tool for China’s carbon peak and carbon-neutral action and an essential part of the country’s promotion of a comprehensive green transformation of the economy and society.This study uses a VAR(Vector Autoregressive)model to analyze the influencing factors of the Beijing carbon emissions trading price from January 2014 to December 2019.The study found that coal prices have the most significant impact on Beijing’s carbon emissions trading prices.Oil prices,industrial development indexes,and AQI(Air Quality Index)impacted Beijing’s carbon emissions trading prices.In contrast,natural gas prices and economic indexes have the most negligible impact.These findings will help decision-makers determine a reasonable price for carbon emissions trading and contribute to the market’s healthy development.

Research on Spatial-Temporal Characteristics and Driving Factor of Agricultural Carbon Emissions in China

Macroscopic grasp of agricultural carbon emissions status, spatial-temporal characteristics as well as driving factors are the basic premise in further research on China’s agricultural carbon emissions. Based on 23 kinds of major carbon emission sources including agricultural materials inputs, paddy ifeld, soil and livestock breeding, this paper ifrstly calculated agricultural carbon emissions from 1995 to 2010, as well as 31 provinces and cities in 2010 in China. We then made a decomposed analysis to the driving factors of carbon emissions with logarithmic mean Divisia index （LMDI） model. The results show：（1） The amount of agricultural carbon emissions is 291.1691 million t in 2010. Compared with 249.5239 million t in 1995, it increased by 16.69%, in which, agricultural materials inputs, paddy ifeld, soil, enteric fermentation, and manure management accounted for 33.59, 22.03, 7.46, 17.53 and 19.39%of total agricultural carbon emissions, respectively. Although the amount exist ups and downs, it shows an overall trend of cyclical rise; （2） There is an obvious difference among regions：the amount of agricultural carbon emissions from top ten zones account for 56.68%, while 9.84%from last 10 zones. The traditional agricultural provinces, especially the major crop production areas are the main source regions. Based on the differences of carbon emission rations, 31 provinces and cities are divided into ifve types, namely agricultural materials dominant type, paddy ifeld dominant type, enteric fermentation dominant type, composite factors dominant type and balanced type. The agricultural carbon emissions intensity in west of China is the highest, followed by the central region, and the east zone is the lowest; （3） Compared with 1995, efifciency, labor and structure factors cut down carbon emissions by 65.78, 27.51 and 3.19%, respectively;while economy factor increase carbon emissions by 113.16%.

Industrial carbon emissions and influencing factors in the Yangtze River Delta urban agglomeration

This paper calculates the industrial carbon emissions of the Yangtze River Delta urban agglomeration over the period 2006-2013. An empirical analysis is conducted to find out the influencing factors of industrial carbon emissions of the Yangtze River Delta urban agglomeration, using a spatial Durbin panel model. The results show that cities with larger industrial carbon emissions often enjoy low annual growth rates, while the cities with smaller ones enjoy higher annual growth rate; There exists a comparatively strong positive correlation in space in per capita carbon emission; urbanization, and total population. GDP per capita and international trade are the main influencing factors of industrial carbon emissions; There are spatial spillover effects on international trade and urbanization of neighboring cities, which have a significant impact on local industrial carbon emissions.

Spatial-temporal Distribution and Influencing Factors of Urban Carbon Emissions under the Background of Carbon Emission Reduction:A Case Study of Guangxi Autonomous Region

Based on the panel data of Guangxi from 2005 to 2017,the spatiotemporal characteristics and determinants of urban carbon emissions in Guangxi were analyzed using the extended STIRPAT model and the Geographically and Temporally Weighted Regression(GTWR)model.The main findings of our research can be summarized as follows.While the total carbon emissions of cities in Guangxi consistently increased from 2005 to 2014,the growth trend slowed after 2014,leading to a stabilization in the total emissions.In addition,there are significant differences in the total carbon emissions among the cities.The central and northeastern regions have higher emissions,while the southwestern region has lower emissions.Finally,there are variations in the degrees and directions of the impacts that factors have on carbon emissions among the different time periods and cities.Urban land use is a key factor driving carbon emissions,and it has a negative impact on most cities in Guangxi.Meanwhile,factors such as industrial structure,population urbanization,population concentration,and economic growth have significant positive effects on carbon emissions in Guangxi.The influence of urban roads on carbon emissions is generally positive,while the degree of openness to the outside world and environmental regulations has relatively weaker impacts on emissions.In summary,in order to promote the low-carbon transition of Guangxi and achieve high-quality development,the cities in Guangxi should implement differentiated urban carbon reduction strategies that are focused on optimizing urban land use and industrial structure.

Potential and Efficiency of Agricultural Pollution Control in China and Its Influential Factors

Agricultural pollution has become the dominant source of water pollution in China and the carbon reduction in agricultural aspect is pressing.Based on list analysis method,the COD,TN and TP in agriculture in 28 provinces in China from 1995 to 2010 were evaluated and compared.By dint of directional distance function,the economics mechanism to reduce carbon emission was discussed.The reduction efficiency and potential of three kinds of pollutants were estimated.The regression indicates that the educational degree,income level and work play a crucial role in carbon emission.

Empirical Analysis on Factors Restricting Low-carbon Agricultural Development in Hubei Province

From the perspective of input into agricultural production, the growth of agricultural production from 1982 to 2010 in Hubei Province was analyzed by using multiple regression method. The results show that the quantity of fertilizer and the total power of machinery used for farming have significantly contributed to agricultural growth in Hubei Province, but they are also main factors restricting low-carbon agricultural development in Hubei Province. Therefore, the key point to develop low-carbon agriculture in Hubei Province currently should be put on how to fertilize rationally, how to innovate the pattern of agricultural production, how to develop agricultural mechanization rationally and so on.

Research on influencing factors of urban building carbon emissions based on STIRPAT model——taking Suzhou as an example

The research on the influencing factors of carbon emissions from urban buildings is of great significance for the reduction of carbon in the urban building sector and even the realization of the city’s the carbon peak and neutrality goals.In this paper,combined with the ridge regression method,the STIRPAT model is used to establish a new model for influencing factors of building carbon emissions in Suzhou,and the factors such as urbanization rate,the number of permanent residents,per capita construction and tertiary industry added value,and per capita disposable income are analyzed.The analysis results show that the urbanization rate is the primary driving factor for building carbon emissions in Suzhou,followed by the number of permanent residents,then the added value of the per capita construction industry and tertiary industry,and finally the per capita disposable income.The conclusions of this paper indicate that industrialization and urbanization have strongly promoted the growth of building carbon emissions in Suzhou.In the future,with the continuous development of industrialization and urbanization and the increase of population,Suzhou City can rationally plan urban development boundaries to promote green and low-carbon transformation and development in the field of urban and rural construction,improve residents’low-carbon awareness,and advocate green and low-carbon behavior of residents to reduce building carbon emissions.

Multi-process production occurs in the iron and steel industry,supporting‘dual carbon'target:An in-depth study of CO_(2)emissions from different processes

Reducing CO_(2)emissions of the iron and steel industry,a typical heavy CO_(2)-emitting sector is the only way that must be passed to achieve the‘dual-carbon’goal,especially in China.In previous studies,however,it is still unknown what is the difference between blast furnace basic oxygen furnace(BF-BOF),scrap-electric furnace(scrap-EF)and hydrogen metallurgy process.The quantitative research on the key factors affecting CO_(2)emissions is insufficient There is also a lack of research on the prediction of CO_(2)emissions by adjusting industria structure.Based on material flow analysis,this study establishes carbon flow diagrams o three processes,and then analyze the key factors affecting CO_(2)emissions.CO_(2)emissions of the iron and steel industry in the future is predicted by adjusting industrial structure The results show that:(1)The CO_(2)emissions of BF-BOF,scrap-EF and hydrogen metallurgy process in a site are 1417.26,542.93 and 1166.52 kg,respectively.(2)By increasing pellet ratio in blast furnace,scrap ratio in electric furnace,etc.,can effectively reduce CO_(2)emissions(3)Reducing the crude steel output is the most effective CO_(2)reduction measure.There is still 5.15×10^(8)-6.17×10^(8) tons of CO_(2)that needs to be reduced by additional measures.

交通碳排放研究综述::核算方法、影响因素及作用机理

交通运输业是碳减排的重要行业,关于交通碳排放核算方法、影响因素及作用机理的研究一直是交通碳排放研究领域的一个热点。本文从交通碳排放量核算方法、交通碳排放影响因素辨识、交通碳排放作用机理分析三个方面来进行综述。在交通碳排放量核算方法部分按照直接获取、“自上而下”法、“自下而上”法、机动车排放模型四种方法进行分类。在交通碳排放影响因素辨识部分从IPAT模型、Kaya恒等式、直接选取三点进行综述,并对近年学者们选取的影响因素进行了总结。在交通碳排放作用机理分析部分综述了脱钩模型、因素分解法、计量经济学方法、系统动力学方法,进而对用不同方法分析的交通碳排放作用机理进行了总结,其中Tapio脱钩模型和对数平均迪氏指数分解法是应用较为广泛的方法。最后,探讨了交通碳排放核算方法、影响因素及作用机理研究中的关键问题,指出本领域的研究难点主要集中于交通碳排放核算方法的优化及统一、影响因素选取体系的形成上。

基于LMDI模型的土地利用碳排放时空差异及影响因素研究——以洞庭湖区为例

运用碳排放系数法和对数平均迪氏指数(LMDI)模型,对1996—2020年洞庭湖区土地利用变化、土地利用碳排放时空差异及其影响因素进行分析。结果表明:(1)土地利用变化整体呈现出“三增三减”特征,建设用地和未利用地的变化程度较为显著,水域和草地次之,耕地和林地的变化程度最小。(2)总净碳排放量呈不断上升趋势,碳排放高值区由中部转向西、东、南部,土地利用碳足迹压力指数始终大于1,以0.54的年均增幅不断上升。(3)人均GDP、单位GDP用地面积和单位土地碳排放强度是影响土地利用碳排放的主要因子,人均GDP和单位GDP用地面积分别成为促进和减缓洞庭湖区碳排放量增长的主要因素。

东北三省农业碳排放时空分异特征及其关键驱动因素

推动农业低碳发展是应对气候威胁和农业面源污染的有效途径。本文基于IPCC和农用物资投入数据核算2000—2019年东北三省农业碳排放,利用空间自相关等方法分析其时空分异特征,通过LMDI指数分解模型和地理探测器探究农业碳排放驱动因素及其交互作用关系。结果表明:1)东北三省2015年农业碳排放总量达到峰值,约为1759.66万t,较2000年(1048.19万t)增加67.88%,年均递增4.53%;研究期整体呈现“先上升后下降”态势,碳排放增量变动可划分为“波动上升期(2000—2009年)—过渡期(2010—2015年)—平稳下降期(2016—2019年)”3个阶段。化肥施用是主要碳源,占比75.12%。2)分解模型测算结果表明,农业生产效率、农业产业结构和农业劳动力规模对碳排放具有抑制作用,其碳减排比例分别为207.31%、21.56%、20.72%;农业经济发展水平对碳排放表现出较强的推动作用,实现349.59%的碳增量。3)相较于单因子来说,农业经济发展水平、农业生产效率与农业产业结构之间交互结果对农业碳排放的影响呈非线性增强特征,农业劳动力规模与其他因素叠加均呈现出双因子增强的作用效果。以上研究结果表明东北三省农业碳排放受周边地区影响且影响程度不断加强,同时碳排放关键驱动因素之间存在协同作用。本研究成果为推动农业低碳发展提供理论基础与政策依据。

海洋渔业碳排放效率的时空演变及影响因素——以北部海洋经济圈为例

促进海洋渔业低碳化、绿色化、可持续发展既是海洋生态文明建设要求,也是促进海洋经济高质量发展的重要手段。从碳排与碳汇视角核算海洋渔业碳排放,采用超效率SBM模型测算2006—2019年北部海洋经济圈海洋渔业碳排放效率,并借助STIRPAT模型分析海洋渔业碳排放效率影响因素。结果显示:(1)2006—2019年北部海洋经济圈海洋渔业碳排放量不断上升;海洋渔业碳排放量区域差异较大;海洋渔业碳排放增长率的变化趋势较为一致。(2)海洋渔业碳排放效率总体水平不高,呈现缓慢下降—波动上升—波动下降变化态势;各项效率指数均具有一定波动性;分地区看,山东整体明显高于河北、辽宁和天津。(3)海洋渔业科技水平是海洋渔业碳排放效率最主要影响因素,海洋渔业规模、海洋经济规模、对外开放程度、海洋产业结构是重要影响因素。

长三角工业减污降碳时空演变及其影响因素研究

工业领域是我国推行减污降碳协同增效的重点领域,探究工业领域减污降碳的时空特征及影响因素,对实现减污降碳协同增效总目标具有重要现实意义.本文以长三角地区41个设区市为研究对象,综合运用耦合协调度模型、空间自相关、时空地理加权回归、地理探测器等方法,对2010−2020年长三角地区工业减污降碳协同效应的时空演变特征及影响因素展开分析,因地制宜地提出工业减污降碳协同推进建议.结果表明:①2010−2020年,长三角地区41个设区市的工业大气污染物排放量平均值大幅下降,工业二氧化碳排放量缓慢增长,长三角地区工业减污降碳协同效应总体处于上升优化态势,工业减污降碳协同效应等级由失调衰退类提至过渡发展类.②2010−2020年,长三角地区工业减污降碳协同效应在空间格局上呈中部高、南北低以及东部高、西部低的分布格局,时空变动上工业减污降碳协同效应高值范围由上海、苏州等长江入海口地区向西转移至南京、无锡、苏州等长江下游地区,其空间集聚特征呈现出集聚−离散−集聚的变化趋势.③规模以上工业总产值、规模以上工业增加值占GDP比重、人均GDP、城镇化率等是影响长三角地区工业减污降碳协同效应的主要因素,对大部分区域产生显著的正向影响,其影响程度存在时空异质性,双因子交叉具有显著的增强作用.研究显示,长三角地区工业减污降碳协同效应存在显著的优化趋势和空间差异,受产业规模结构影响较大,亟需从统筹优化减污降碳协同目标、加强重点区域协同控制、加快工业行业绿色发展等方面协同推进.

基于LMDI和系统聚类的电力行业碳排放影响因素分析

电力行业作为直接使用一次能源的最大部门,是落实我国碳减排目标的重点行业。为厘清电力行业碳排放的主要驱动或抑制来源,论文构建了LMDI模型,从国家及省域两个层面对2006—2020年电力行业碳排放的影响因素进行了分解。研究结果表明,从国家及省域两个层面来看,经济发展效应均为电力碳排放的主要促进因素,火电燃料转化效应和产业结构效应均为电力碳排放的抑制因素,电源结构效应、工业电耗强度效应在全国层面为电力碳排放的抑制因素,但在各省份中的影响效果及程度各有不同。论文以主要抑制因素为变量,利用系统聚类法将30个省份划分为六大区域,针对各区域影响因素的作用效果提出了因地制宜的减排政策。

京津冀建筑业碳排放区域差异及影响因素研究

以京津冀地区为研究对象,构建建筑业全过程碳排放测算模型分析京津冀地区建筑业碳排放的区域差异,利用STIRPAT模型进一步探讨人口总量、城镇化水平、人均GDP、建筑产业结构效应、能源结构效应、能源消耗强度、劳动生产率7个因素对京、津、冀建筑业碳排放的影响。结果表明:京津冀地区建筑业碳排放总量大体呈现先增后减再稳定的态势,各因素对京津冀建筑业碳排放的影响存在地区差异,除劳动生产率抑制建筑业碳排放增长,其余6个因素均起促进作用,其中城镇化水平对北京市和天津市的促进作用最强,人口总量对河北省的促进作用最强。基于此,根据各地区碳排放影响因素的影响情况制定差异化减排对策。

“双碳”目标下旅游碳排放影响因素变化规律研究

以旅游碳排放为研究对象,以南京为起点,通过设计四种不同的旅游线路,重点分析交通方式、住宿形式、旅游活动内容等不同旅游环节对碳排放的影响变化规律。研究结果表明,与近郊游相比,省际游交通碳排放最高可达近郊游的18.9倍和73.5倍。在同样选择快捷酒店的情况下,省际游住宿碳排放量是近郊游的8.9倍和13.3倍。随着旅游距离的增加,交通方式碳排放占比逐渐增大。近郊游交通方式碳排放占比为25.4%,而省际游三亚交通碳排放占比高达53.4%,超过了旅游活动所产生的碳排放占比。1次近郊游产生的碳排放所需要的碳汇森林面积约168.8 m2,而省际游青岛和海南三亚所产生的碳排放需要的碳汇森林面积高达1423.77 m2和2948.74 m2,省际游所需碳汇森林面积是近郊游的8.43倍和17.47倍。研究结论对旅游政策的制定、节假日规划以及旅游线路规划具有很好的指导和借鉴作用。

安徽省农业生产碳排放测算及驱动因子的典型相关分析

探索农业生产碳排放强度驱动因子,对制定低碳农业发展规划及目标具有重要启示意义。运用IPCC碳排放测度模型,测算了2005—2020年安徽省农业生产碳排放情况,采用典型相关分析方法,考察了农业生产碳排放效率驱动因子。结果表明,安徽省农业生产碳排放由2005年的987.40万t攀升至2020年的1227.59万t,年均增长1.46%。农业生产碳排强度由2005年的1.21 t/万元下降至2020年的0.49 t/万元,年均下降5.85%;农业生产碳排放与其强度呈显著剪刀差态势,两者间夹角达33.52°。农业科技创新、农机化水平、农业生产结构与农业生产碳排放强度典型相关系数分别为-0.652、-1.728、-0.562,均为农业生产碳排放强度下降的动力因子;规制政策、城镇化水平、农村经济发展水平与农业生产碳排放强度典型相关系数分别为0.085、0.619、1.232,为农业生产碳排放强度下降的制约因子。基于研究结果,提出了安徽省发展低碳农业的政策建议。