Data error propagation in stacked bioclimatic envelope models

Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models(BEMs).The approach can be used to investigate patterns and processes of species richness.If data limitations on individual species distributions are inevitable,but how do they affect inferences of patterns and processes of species richness?We investigate the influence of different data sources on estimated species richness gradients in China.We fitted BEMs using species distributions data for 334 bird species obtained from(1)global range maps,(2)regional checklists,(3)museum records and surveys,and(4)citizen science data using presence-only(Mahalanobis distance),presence-background(MAXENT),and presence–absence(GAM and BRT)BEMs.Individual species predictions were stacked to generate species richness gradients.Here,we show that different data sources and BEMs can generate spatially varying gradients of species richness.The environmental predictors that best explained species distributions also differed between data sources.Models using citizen-based data had the highest accuracy,whereas those using range data had the lowest accuracy.Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty.When multiple data sets exist for the same region and taxa,we advise that explicit treatments of uncertainty,such as sensitivity analyses of the input data,should be conducted during the process of modeling.

广东省煤炭消费的动态演变及其驱动机制

Guangdong Province,as one of China’s fast-developing regions,an important manufacturing base,and one of the national first round low-carbon pilots,still faces many challenges in controlling its total energy consumption.Coal dominates Guangdong’s energy consumption and remains the major source of CO_(2).Previous research on factors influencing energy consumption has lacked a systematic analysis both from supply side(factors related to scale,structure,and technologies)and demand side(investment,consumption,and trade).This paper develops the logarithmic mean Divisia index(LMDI)method that focuses on the supply side and the structural decomposition analysis(SDA)method that focuses on the demand side to systematically identify the key factors driving coal consumption in Guangdong.Results are as follows:(1)Supply side analysis indicates that economic growth has always been the most important factor driving coal consumption growth,while energy intensity is the most important constraining factor.Industrial structure and energy structure have different impacts on coal consumption control during different development phases.(2)Demand side analysis indicates that coal is consumed mainly for international exports,inter-provincial exports,fixed capital formation,and urban household.(3)Industries with the fastest coal consumption growth driven by final demand have experienced significant shifts.Increments in industrial sectors were mainly driven by inter-provincial exports and urban household consumption in recent years.(4)Research on energy consumption in subnational regions under China’s new development pattern of“dual circulation”should not only focus on exports in the context of economic globalization but also pay more attention to inter-provincial exports on the background of strengthened interregional connections.

基于夜间灯光数据的新疆城市能源消费碳排放时空演化及影响因素

This essay combines the Defense Meteorological Satellite Program Operational Linescan System(DMSP-OLS)nighttime light data and the Visible Infrared Imaging Radiometer Suite(VIIRS)nighttime light data into a“synthetic DMSP”dataset,from 1992 to 2020,to retrieve the spatio-temporal variations in energy-related carbon emissions in Xinjiang,China.Then,this paper analyzes several influencing factors for spatial differentiation of carbon emissions in Xinjiang with the application of geographical detector technique.Results reveal that(1)total carbon emissions continued to grow,while the growth rate slowed down in the past five years.(2)Large regional differences exist in total carbon emissions across various regions.Total carbon emissions of these regions in descending order are the northern slope of the Tianshan(Mountains)>the southern slope of the Tianshan>the three prefectures in southern Xinjiang>the northern part of Xinjiang.(3)Economic growth,population size,and energy consumption intensity are the most important factors of spatial differentiation of carbon emissions.The interaction between economic growth and population size as well as between economic growth and energy consumption intensity also enhances the explanatory power of carbon emissions’spatial differentiation.This paper aims to help formulate differentiated carbon reduction targets and strategies for cities in different economic development stages and those with different carbon intensities so as to achieve the carbon peak goals in different steps.

粤港澳大湾区风险资本的流动格局、演变及机制

As an important innovation flow,venture capital has been examined in urban network research.However,the segmentation of capital categories and the cross-scale connection of capital remain scarcely analyzed.This study focuses on the structure and industry differentiation of venture capital flows in the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)and its cross-scale network characteristics.Based on a venture capital database covering capital amount,investment subject address information,and industry information(2000-2018),this article examines the spatial distribution and network structure of venture capital in the GBA by means of a distance-based test of spatial concentration approach and social network analysis.Key findings show that:(1)Venture capital institutions and startups in the GBA present a high-concentration distribution pattern.In the past 20 years,venture capital activities in the GBA have substantially increased,forming a complex urban network structure with Guangzhou,Shenzhen,and Hong Kong as the core of this network.(2)Different types of venture capital show significantly different urban network structures,with manufacturing,the Internet industry,the financial sector,the cultural media industry,and the medical and health industry as the five industry types with the largest capital flow in the GBA.(3)Cross-scale research on the venture capital network reveals the position of the GBA as a capital hub in China,which forms a dense venture capital connection network with major cities on a national scale.(4)The network structure of venture capital in the GBA is influenced by multi-dimensional proximity,institutional factors,urban economy,and path dependence.Along with these three key mechanisms,the GBA has grown into a national-scale and even global-scale venture capital center.