Analysis on Temporal Change and Grey Relation of Transportation Carbon Emissions in Jilin Province

PurposeThe purpose of this paper is to construct some negative grey relational analysis models to measure the relationship between reverse sequences.Design/methodology/approachThe definition of reverse sequence has been given at first based on analysis of relative position and change trend of sequences. Then, several different negative grey relational analysis models, such as the negative grey similarity relational analysis model, the negative grey absolute relational analysis model, the negative grey relative relational analysis model, the negative grey comprehensive relational analysis model and the negative Deng’s grey relational analysis model have been put forward based on the corresponding common grey relational analysis models. The properties of the new models have been studied.FindingsThe negative grey relational analysis models proposed in this paper can solve the problem of relationship measurement of reverse sequences effectively. All the new negative grey relational degree satisfying the requirements of normalization and reversibility.Practical implicationsThe proposed negative grey relational analysis models can be used to measure the relationship between reverse sequences. As a living example, the reverse incentive effect of winning Fields Medal on the research output of winners is measured based on the research output data of the medalists and the contenders using the proposed negative grey relational analysis model.Originality/valueThe definition of reverse sequence and the negative grey similarity relational analysis model, the negative grey absolute relational analysis model, the negative grey relative relational analysis model, the negative grey comprehensive relational analysis model and the negative Deng’s grey relational analysis model are first proposed in this paper.

Effectively exploring the impacts of urban spatial structures on carbon dioxide emissions is important for achieving low-carbon goals. However, most previous studies have examined the impact of urban spatial structure on total carbon emissions based only on polycentricity. Fine-grained studies on subsectoral carbon emissions and other dimensions of urban spatial structure are lacking. Therefore, our study comprehensively explores the impact of urban dispersion and polycentricity on total carbon emissions and carbon emissions of four subsectors (industry, power, civilian, and transportation) from 2012 to 2017 while considering the effects of city size. Results reveal that the nighttime light data is useful for measuring urban spatial structure, and a polycentric, decentralized urban spatial structure correlates with the reduced total carbon emissions and transportation carbon emissions. Meanwhile, a decentralized urban spatial structure gives rise to lower industrial carbon emissions and civilian carbon emissions, whereas a multicenter urban spatial structure contributes to minimizing carbon emissions from power systems. However, in small and medium-sized cities, urban spatial structure differently affects the total carbon and transportation carbon emissions.

The energy-related carbon emissions of China’s manufacturing increased rapidly, from 36988.97 × 104 tC in 1996 to 74923.45 × 104 tC in 2012. To explore the factors to the change of the energy-related carbon emissions from manufacturing sector and the decoupling relationship between energy-related carbon emissions and economic growth, the empirical research was carried out based on the LMDI method and Tapio decoupling model. We found that the production scale contributed the most to the increase of the total carbon emissions, while the energy intensity was the most inhibiting factor. And the effects of the intrastructure and fuel mix on the change of carbon emissions were relatively weak. At a disaggregative level within manufacturing sector, EI subsector had a greater impact on the change of the total carbon emissions, with much more potentiality of energy conservation and emission reduction. Weak decoupling of manufacturing sector carbon emissions from GDP could be observed in the manufacturing sector and EI subsector, while strong decoupling state appeared in NEI subsector. Several advices were put forward, such as adjusting the fuel structure and optimizing the intrastructure and continuing to improve the energy intensity to realize the manufacturing sustainable development in low carbon pattern.

This study utilizes the China Statistical Yearbook 2023 as the data basis to explore the temporal and spatial changes of total agricultural carbon emissions and the importance of reducing agricultural carbon emissions to solve the greenhouse effect in the context of China's transition to green and low-carbon agricultural production. This is achieved through statistical analysis and calculations of fluctuations in agricultural output value, cultivated land area, irrigated area, fertilizer application, sown area of crops, and production from 2014 to 2022. The results indicate the following: 1) The total carbon emissions from agricultural land use in China exhibited a trend of initial increase followed by a decrease, reaching a peak of 128.9831 million tons in 2015; 2) Changes in cultivated land area contributed the highest average carbon emissions, followed by fertilizer application, accounting for 45.39% and 38.35% of the average carbon emissions in 2022, respectively; 3) The carbon emission reduction in national crop production is significantly influenced by policies, and controlling the amount of fertilizer use can effectively reduce carbon emissions from national crop production. The research findings provide a theoretical foundation for the transition to green and low-carbon agricultural production.

Based on the traffic and transportation energy consumption, the carbon emissions of traffic and transportation energy consumption are obtained by using the estimation model of carbon emissions from 1999 to 2011 in Jilin Province, and the dynamic changes and the Environmental Kuznets Curve (EKC) of carbon emissions are analyzed. The result indicates that the carbon emission of traffic and transportation energy consumption increased continuously from 99.3750×104 t to 331.8255×104 t between 1999 and 2011 in Jilin Province, the change process is divided into three stages which include the stage of the stationary growth phase, accelerated growth stage and slow growth stage, the large consumption of diesel energy is the main reason of the rapid growth in carbon emissions. The EKC of carbon emission shows the inverted U shape roughly and the turning point appeared in 2011, after 2011, carbon emissions will decrease along with the economic growth. Based on the STIRPAT model, the study reveals that elasticity coefficients of driving factors such as population, per capita GDP, the unit GDP energy consumption, the investment of traffic and transportation, city rate, the number of private cars are 0.23440.2202-0.22470.16570.2864 and 0.2163, respectively. Jilin Province must implement effective measures to change the existing development mode of traffic and transportation, change the energy structure, and increase the innovation of scientific and technological, to strive for the realization of negative growth in carbon emissions of traffic and transportation energy consumption.

PDF HTML阅读 XML下载 导出引用 引用提醒 我国城市发展与能源碳排放关系的面板数据分析 DOI: 10.5846/stxb201911292591 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学重点基金项目（71533005）；国家重点研发项目（2017YFF0207303） The impact of urbanization on carbon emissions: Analysis of panel data from 158 cities in China Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:城市化与城市能耗及其碳排放密切相关，城市发展过程中的人口城市化进程和产业总量与结构调整都是能源碳排放变化的主要驱动因素。以2006-2015年全国158个地级城市的面板数据为基础，从总量变化趋势和空间变化趋势两个角度分析了研究期内的我国城市发展特征及能源碳排放特征；并利用面板计量分析方法研究了城市发展因素对城市总能耗、总能耗碳排放、单位能耗碳排放量的驱动特征。结果表明：城市化每提升0.095%，总能耗上升1%。虽然城市总能耗及能耗碳排放在降低，但是单位能耗碳排放在增加；第二产业和第三产业发展对总能耗及能耗碳排放的驱动作用大；城市第三产业的发展有利于能源结构优化调整等；并基于研究发现给出一些政策建议。 Abstract:Urbanization is closely related to urban energy consumption and associated carbon emissions. The process of population urbanization and industrial structure adjustment in urban development are the main drivers of changes in carbon emissions. Based on the panel data of 158 prefecture-level cities in China from 2006 to 2015, this study analyzes the urban development characteristics and energy carbon emission characteristics of China from total volume and spatial variation. The study uses panel measurement to analyze the driving characteristics of urban development factors on total urban energy consumption, total carbon emissions, and carbon emissions per unit energy consumption. The results show that for every 0.095% increase in urbanization, the total energy consumption increases by 1%. Although the total urban energy consumption and carbon emissions are decreasing, the carbon emissions per unit energy consumption are increasing. The total energy consumption of secondary and tertiary industries is also increasing. The development of tertiary industries in the city is beneficial for the optimization and adjustment of the energy structure. Based on the findings, some policy suggestions are proposed. 参考文献 相似文献 引证文献

It is one of the goals of current social development to achieve regional high-quality development and meet people's needs for an ecological environment. Taking Gansu Province as an example, the spatial and temporal changes in carbon emissions in Gansu Province from 1992 to 2021 and their influencing factors are analyzed based on a multi-scale perspective using spatial autocorrelation, cold hotspots, and geographic probes. The results showed that： ① The total carbon emissions in Gansu Province fluctuated and increased from 1992 to 2021, but the growth rate was decreasing. ② In terms of the type of growth, low growth was mainly concentrated in the relatively economically developed districts and counties, and high growth was mainly concentrated in the economically underdeveloped districts and counties, regardless of the city and state scales or county scales. ③ Carbon emissions in Gansu Province showed a significant global spatial positive correlation at the county scale, with the aggregation capacity weakening and then increasing, the high-high aggregation area moving from Longzhong to Longdong, and the low-low aggregation area distributed in the south of Gansu. ④ The influence of factors on carbon emissions tended to grow with the enlargement of the study scale, and the influence of regional GDP on the spatial differentiation of carbon emissions has been maintained at a high level. Whether at the city-state scale or the county scale, the explanatory power of a single factor on carbon emissions was weaker than the interaction of the factors.

BackgroundWater and land resources are important for maintaining the sustainable development of society. However, with the utilization of water and land resources, a large amount of carbon emissions will be generated. Therefore, studying carbon emissions under the water-land-carbon connection is of great significance for achieving “dual carbon goals”. This paper first calculated the land use carbon emissions and the total carbon emissions in Shandong Province. Secondly, the carbon emission economic contribution coefficient (EC), carbon water coefficient (CWC), carbon emission intensity (CI), and coefficient of variation (CV) were constructed. The center of gravity-standard deviation ellipse was used to determine the spatio-temporal distribution characteristics of carbon emissions. Finally, the Kaya-LMDI model was used to investigate the factors that influence carbon emissions.Results(1) The land use and total carbon emissions of the Provincial Capital Economic Circle (PEC) are more than those of the Jiaodong Economic Circle (JEC) and those of Lunan Economic Circle (LEC). For EC, PEC is greater than LEC is greater than JEC. For CWC, JEC is greater than PEC is greater than LEC. For CI, LEC is greater than PEC is greater than JEC. (2) The CV of carbon emissions in the province is at a low level, indicating a small fluctuation in carbon emissions. The spatial–temporal distribution of the land use carbon emissions is generally from northeast to southwest, and the center of gravity migration track is from northwest to northeast to southwest. The distribution of the total carbon emissions changes from northeast-southwest to southeast-northwest, and the shifting track is east-southwest. (3) Carbon emission efficiency effect, land economy effect, and population effect promote carbon emission; water use intensity effect and per capita land use effect inhibit carbon emission.ConclusionsPEC gives priority to promoting the adjustment of industrial structure and the development of renewable energy; JEC strengthens the application of water-saving and recycling technologies; LEC optimizes land efficiency, develops low-carbon agriculture and strictly controls high energy-consuming projects. This result provides a new perspective and practical basis for urban collaborative carbon reduction.

Based on the calculation model of agricultural carbon emissions, this study focused on the three aspects of agricultural material inputs, rice cultivation and livestock breeding to calculate agricultural carbon emissions, and analyzed their features of temporal changes. The influence factors of agricultural carbon emissions were studied based on STIRPAT model. The results indicated that the temporal change of agricultural carbon emissions in Baicheng City was divided into three phases: slow-growth phase, rapid-growth phase and fluctuation phase, and showed a continuous growth, agricultural carbon emissions intensity first went up and then down, with the maximum was 6.1921t/hm2 in 2008. Population, GDP per capita, agricultural machinery power, agricultural output ratio, rural investment, urbanization rate, net income of farmers per capita were the influence factors of agricultural carbon emissions, and their elasticity coefficients were 0.2605, 0.0874, 0.1126, -0.0766, 0.0353, 0.2083 and 0.1128, respectively.

Reducing agricultural carbon emissions (ACE) is a key point to achieve green and sustainable development in agriculture. Based on the ACE statistics of Jilin Province in China from 1998 to 2018, this article considers the sources of ACE in depth, and fourteen different carbon sources are selected to calculate ACE. Besides, the paper explores the variation characteristics of ACE in Jilin Province, their structure, and the relationship between the intensity and density of the dynamic changes in ACE in the province in terms of time. Finally, this paper uses the Kaya identity and logarithmic mean Divisia index (LMDI) to analyze the influential factors in ACE. The results show the following: (1) During 1998–2018, the amount of ACE in Jilin Province increased, with an average annual growth rate of 1.13%. However, the chain growth rate has been negative in recent years, which reflects that carbon emission reduction has been achieved to a certain extent. (2) The characteristics of ACE in Jilin Province during the years is that of the low-intensity, high density category. Furthermore, agricultural resource input is the main source of the planting industry’s carbon emissions. From the perspective of animal husbandry, the proportion of CH4 decreased, while the proportion of N2O is relatively stable. (3) Based on the LMDI decomposition model, production efficiency, industrial structure, and labor are the three main factors that reduce ACE in Jilin Province. The economic level is the main factor of ACE, and it will be the most important factor leading to an increase in ACE in the short term. On the basis of comprehensive analysis, this article puts forward reasonable suggestions in terms of policy improvement, production mode and industrial structure adjustment, technological innovation, and talent introduction.

Land use changes lead to changes in the functions of different types of carbon sources and sinks, which are key sources of carbon emissions. The study of carbon emissions and its influencing factors in the Aksu River Basin from the perspective of land use change is of great importance for the promotion of integrated protection and restoration of mountains, water, forests, fields, lakes, grasslands, sand, and ice in the basin and to help achieve the goal of carbon peaking and carbon neutrality. Based on four periods of land use data and socio-economic data from 1990 to 2020, the total carbon emissions from land use were measured, and the spatial and temporal trajectories of carbon emissions and their influencing factors were explored. The results showed that:① from 1990 to 2020, arable land, forest land, construction land, and unused land showed a general increasing trend, whereas grasslands and water areas showed a decreasing trend. The spatial change in land use types was mainly characterized by the conversion of grasslands and unused land into arable land, and 83.58 % of the arable land conversion areas were concentrated in the southwest of Wensu, Aksu, and the northern part of Awat. ② The total net carbon emissions in the basin showed a continuous growth trend from 1990 to 2020, with a cumulative increase of 14.78×104 t. The increase in arable land was a key factor causing an increase in net carbon emissions in the basin. ③ The spatial distribution pattern of land use carbon emissions in the basin was high in the middle and low in the fourth, with significant changes in net carbon emissions mainly in the southern part of Wensu, Aksu, Awat, and Alaer. ④ Human activities had the strongest driving effect on land use carbon emissions, with their effects gradually increasing from east to west. The contribution of average annual temperature to land use carbon emissions was mainly concentrated in the eastern part of Aksu and the northern part of Awat, whereas average annual rainfall had a strong inhibitory effect on the northern part of Wensu and the western part of Aheqi.

The changes in the carbon emissions in China's provincial construction industries are of high complexity. It is essential to understand the changes in the construction carbon emissions (CCEs) in China on the provincial scale. This study evaluates the factors and structural paths of the changes in provincial CCEs in China between 2012 and 2017 using the structural path decomposition analysis. The results show that the emission intensity effect and production structure effect contributed greatly to the reduction of CCEs across various regions, while the final demand effect had contrary impacts. The local nonmetallic mineral products industry (c13), metal smelting and pressing industry (c14), and electricity industry (c24) generally contributed significantly to the emission intensity effect, production structure effect, and final demand effect across most regions. The consumption of local c13, c14, and c24 by the construction industry (c27), namely “local c13→c27”, “local c14→c27”, and “local c24→c27” were generally the important structural paths of the CCEs changes across various regions. Nonlocal industries such as Hebei c14 and nonlocal structural paths such as “Hebei c14→c27” contributed substantially to the CCEs changes in many regions such as Beijing. The emission intensity effect, first-order production structure effect, and final demand effect typically dominated the effects of the critical structural paths of the CCEs changes across various regions. This study can help policymakers better understand the changes in China's provincial CCEs to formulate region-specific emission reduction measures and provide a comprehensive reference for related research.

A Quantitative Analysis of Carbon Emissions Reduction Ability of Transportation Structure Optimization in China

Location optimization of a competitive distribution center for urban cold chain logistics in terms of low-carbon emissions

Transportation is an important source of carbon emissions in China. Reduction in carbon emissions in the transportation sector plays a key role in the success of China’s energy conservation and emissions reduction. This paper, for the first time, analyzes the drivers of carbon emissions in China’s transportation sector from 2000 to 2015 using the Generalized Divisia Index Method (GDIM). Based on this analysis, we use the improved Tapio model to estimate the decoupling elasticity between the development of China’s transportation industry and carbon emissions. The results show that: (1) the added value of transportation, energy consumption and per capita carbon emissions in transportation have always been major contributors to China’s carbon emissions from transportation. Energy carbon emission intensity is a key factor in reducing carbon emissions in transportation. The carbon intensity of the added value and the energy intensity have a continuous effect on carbon emissions in transportation; (2) compared with the increasing factors, the decreasing factors have a limited effect on inhibiting the increase in carbon emissions in China’s transportation industry; (3) compared with the total carbon emissions decoupling state, the per capita decoupling state can more accurately reflect the relationship between transportation and carbon emissions in China. The state of decoupling between the development of the transportation industry and carbon emissions in China is relatively poor, with a worsening trend after a short period of improvement; (4) the decoupling of transportation and carbon emissions has made energy-saving elasticity more important than the per capita emissions reduction elasticity effect. Based on the conclusions of this study, this paper puts forward some policy suggestions for reducing carbon emissions in the transportation industry.