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.

Spatial and temporal variation of water clarity in typical reservoirs in the Beijing-Tianjin-Hebei region observed by GF 1-WFV satellite data

Rapidly monitoring regional water quality and the changing trend is of great practical and scientific significance,especially for the Beijing-Tianjin-Hebei(BTH)region of China where water resources are relatively scarce and inland water bodies are generally small.The remote sensing data of the GF 1 satellite launched in 2013 have characteristics of high spatial and temporal resolution,which can be used for the dynamic monitoring of the water environment in small lakes and reservoirs.However,the water quality remote sensing monitoring model based on the GF 1 satellite data for lakes and reservoirs in BTH is still lacking because of the considerable differences in the optical characteristics of the lakes and reservoirs.In this paper,the typical reservoirs in BTH-Guanting Reservoir,Yuqiao Reservoir,Panjiakou Reservoir,and Daheiting Reservoir are taken as the study areas.In the atmospheric correction of GF 1-WFV,the relative radiation normalized atmospheric correction was adopted after comparing it with other methods,such as 6 S and FLAASH.In the water clarity retrieval,a water color hue angle based model was proposed and outperformed other available published models,with the R 2 of 0.74 and MRE of 31.7%.The clarity products of the four typical reservoirs in the BTH region in 2013-2019 were produced using the GF 1-WFV data.Based on the products,temporal and spatial changes in clarity were analyzed,and the main influencing factors for each water body were discussed.It was found that the clarity of Guanting,Daheiting,and Panjiakou reservoirs showed an upward trend during this period,while that of Yuqiao Reservoir showed a downward trend.In the influencing factors,the water level of the water bodies can be an important factor related to the water clarity changes in this region.

Spatial and temporal heterogeneity of the impact of per capita income on household indirect carbon emissions in western China

With China entering the stage of high-quality development,the issue of carbon emission has become a hot research topic.This paper analyzes the different temporal and spatial effects of per capita income on household indirect carbon emissions in western China.Based on the data of Chinese Family Panel Studies(CFPS)in 2016 and 2018 in the western China,this paper uses Regression analysis and Bayesian correlation analysis to study the relationship between per capita income and household indirect carbon emissions.The results showed that the indirect carbon emissions generated by the expenditure on food,housing and household equipment in the household consumption structure in the western China were relatively high.In 2016-2018,the per capita income and per capita household consumption indirect carbon emissions in the western China showed an increasing trend.There was a positive correlation between per capita income and indirect carbon emissions of per capita household consumption,and its correlation was gradually enhanced in time dimension.In the spatial dimension,the household indirect carbon emissions in Yunnan,Qinghai,Guangxi Zhuang and Ningxia in the western China were greatly affected by per capita income,while the household indirect carbon emissions in Guizhou was least affected by per capita income.Finally,the paper puts forward some problems that we should consider in the process of facing the per capita income growth and climate change:the collection of carbon tax,the optimization of household consumption structure,the research and development of low-carbon products,and the differentiated carbon reduction.

A hybrid model for high spatial and temporal resolution population distribution prediction

The accurate prediction of population distribution is crucial for numerous applications,from urban planning to epidemiological modelling.Using one-week data collected from open and multiple sources,including telecommunication activity,weather,point of interest,buildings,roads,and land use in Milan,Italy,we develop a hybrid method combining cellular automata(CA)and long short-term memory(LSTM)to predict population distribution with fine temporal and spatial granularity.Specifically,the convolutional autoencoder and LightGBM are applied to identify missing building types based on the pedestrian shed.The LSTM learns the transition rules of CA and Shapley additive explanations value is used for variable importance analysis.Results demonstrate that the combination of convolutional autoencoder and LightGBM is effective in building type prediction.The proposed model for population distribution prediction outperforms LSTM,the combination of CA and neural network,and the combination of CA and LightGBM by at least 5–10%.A variable importance analysis reveals that temporal variables are the most significant for prediction,followed by spatial and natural variables.The order of hour,housing-related variables,and types of precipitation are the most important variables in each category.

Spatio-temporal characteristics of human activities using location big data in Qilian Mountain National Park

Human activities significantly impact the environment.Understanding the patterns and distribution of these activities is crucial for ecological protection.With location-based technology advancement,big data such as location and trajectory data can be used to analyze human activities on finer temporal and spatial scales than traditional remote sensing data.In this study,Qilian Mountain National Park(QMNP)was chosen as the research area,and Tencent location data were used to construct time series data.Time series clustering and decomposition were performed,and the spatio-temporal distribution characteristics of human activities in the study area were analyzed in conjunction with GPS trajectory data and land use data.The study found two distinct human activity patterns,Pattern A and Pattern B,in QMNP.Compared to Pattern B,Pattern A had a higher volume of location data and clear nighttime peaks.By incorporating land use and trajectory data,we conclude that Pattern A and Pattern B represent the activity patterns of the resident and tourist populations,respectively.Moreover,the study identified seasonal variations in human activities,with human activity in summer being approximately two hours longer than in winter.We also conducted an analysis of human activities in different counties within the study area.

Monitoring travel patterns in German city regions with the help of mobile phone network data

This paper discusses the possibility to use mobile phone network data to monitor spatial policies in land use and transport planning.Monitoring requires robust time series and reproducible concepts linking spatial policies to monitoring outcomes,a requirement differing from current literature where mobile phone data analysis is exemplified in selected areas with privileged data access.Concepts need to serve the evaluation of policy objectives,for example in regional or local area plans.In this study,we,therefore,extend the application of mobile phone network data to monitoring applications comparing urban settlement types and their characteristic mobility patterns.To accomplish this,we link mobile phone records with urban classifications and transport network data,using both visual and computational approaches to mine the data.The article presents comparisons of travel patterns for selected monocentric and polycentric city regions in Germany,testing hypotheses of transit-oriented regional development,as well as testing for congestion risks in the transport network.The results help us to gain a more detailed understanding of spatial and temporal patterns in mobility for different urban types and assess future potentials for monitoring spatial policies with mobile phone network data.