中国旅游业碳排放效率趋同演变及其趋势预测

低碳旅游是实现旅游业可持续发展的必然之路,准确把握中国旅游业碳排放效率趋同演变及其发展趋势,对中国“双碳”目标的实现具有重要的意义。基于超效率—SBM模型对2009—2019年中国旅游业碳排放效率科学测度,再采用空间自相关分析与时空Markov链,检验其趋同效应并深入探析其时空趋同特征,最后结合Markov链的无限分布矩阵,科学预测中国旅游业碳排放效率的发展趋势。结果表明：(1)时间特征上,2009—2019年中国旅游业碳排放效率呈“过山车式”波动上升态势,东部效率均值大于西部大于中部。研究期内效率均呈显著的俱乐部趋同分布,从2009—2014年偏向于较低效率“单峰”趋同向2015—2019年偏向于低效率和高效率的“双峰”趋同演变,且相邻效率等级俱乐部更容易发生转移,其中后期比前期的俱乐部趋同效应更强。(2)空间特征上,不同空间滞后条件下,中国旅游业碳排放效率均呈显著的俱乐部趋同分布,但趋同程度随滞后水平的提升有所减弱。空间滞后水平越高,效率向上转移可能性越大。(3)省域效率转移上,大多中西部省份效率保持平稳,但部分沿海发达省份和西部省份实现向上转移,仅少数中东部省份向下转移。(4)省...     

中国生态效率时空动态分布与区域差异

生态效率的积极发展对生态环境和经济协同发展具有重要意义。为研究中国生态效率的发展情况,使用超效率SBM模型对中国2005—2020年省域生态效率进测算,并通过核密度函数、基尼系数、σ收敛和Markov转移矩阵进行时空动态分布和区域差异性分析。研究结果表明：(1)中国生态效率呈现先下降后增长的趋势,北京、青海、广东、海南、上海、江苏、福建、河南、云南生态效率发展较好。(2)六大地理区域生态效率总体差异较大,区域间差异和超变密度是总体差异的主要来源。华北地区生态效率发展较好,东北地区生态效率最低。西北地区区域内部差异最大,东北地区区域内差异最小。中国省域及区域生态效率发展均不具有收敛特征。(3)全国及区域生态效率从t年向t+1年向同水平生态效率转移概率最大,2021—2025年省域生态效率差距仍旧较大,整体水平下降。     

长江经济带自然资本使用量的空间效应及影响因素

厘清区域自然资本的使用情况及其空间效应和影响因素,对实现区域可持续发展具有重要的理论和现实意义.本研究以2008-2018年长江经济带112个地级市为研究区域,利用三维生态足迹模型、探索性空间数据分析、空间马尔科夫链和空间面板杜宾模型,分析区域总体和分地类自然资本流量和存量占用量的空间效应和影响因素.结果 表明:研究期...     

Dynamics of China's regional carbon emissions under gradient economic development mode

Regional economic development in China is extremely uneven, which leads to the variation of the status quo and development trend of carbon emissions in different regions, and thus has a huge impact on the construction of the future carbon trading market, in particular the allocation of emission reduction targets. In this article, we adopt a spatial econometric approach to analyze the spatial characteristics of China's regional carbon emissions under its gradient economic development mode. Results indicate that carbon emissions show strong spatial dependence and convergence across regions. Specifically, carbon emissions of different regions in China are characterized by club convergence, as the country's gradient developing mode has enhanced the spatial agglomeration effects. Furthermore, we analyze the evolving trends of regional emission shares, and build a basic framework for the allocation of regional emission reduction targets in China's future carbon emissions trading market.     

Hidden Markov Models and Animal Behaviour

This paper proposes the use of hidden Markov time series models for the analysis of the behaviour sequences of one or more animals under observation. These models have advantages over the Markov chain models commonly used for behaviour sequences, as they can allow for time-trend or expansion to several subjects without sacrificing parsimony. Furthermore, they provide an alternative to higher-order Markov chain models if a first-order Markov chain is unsatisfactory as a model. To illustrate the use of such models, we fit multivariate and univariate hidden Markov models allowing for time-trend to data from an experiment investigating the effects of feeding on the locomotory behaviour of locusts (Locusta migratoria).     

Exploring the Global Journey of Nickel with Markov Chain Models

Markov chain (MC) modeling is a versatile tool in policy analysis and has been applied in several forms to analyze resource flows. This article builds on previous discussions of the relationship among absorbing Markov chains (AMCs), material flow analysis (MFA), and input‐output (IO) analysis, and presents a full‐scale application of MC modeling for a particular globally relevant, nonrenewable resource, namely nickel. The MC model presented here is built on comprehensive, recently compiled nickel flow data for 52 geographic regions. Considering all possible cycles of recycling and reuse, nickel extracted in 2005 is estimated to have a technological lifetime of 73 ± 7 years. During its global journey, nickel enters use, for some application somewhere in the world, an average of three times, the largest share of which occurs in China. Nickel entering fabrication in 2005 is estimated to enter use approximately four times. Over time, nickel is lost to the environment and as a tramp element in carbon steel; the final distribution of nickel among these absorbing states is 78% and 22%, respectively. Of all the nickel in ore extracted in 2005, fully 28% will eventually end up in the tailings, slag, and landfills of China. MC results are also combined with geographically specific life cycle inventory data to determine the overall energy invested in nickel during its many cycles of use. MCs provide a powerful tool for tracking resources through the network of global production, use, and waste management, and opportunities for further integration with other modeling efforts are also discussed.     

Efficiency of photochemical stages of photosynthesis in purple bacteria (A critical survey)

Based on currently available data, the energy transfer efficiency in the successive photophysical and photochemical stages has been analyzed for purple bacteria. This analysis covers the stages starting from migration of the light-induced electronic excitations from the bulk antenna pigments to the reaction centers up to irreversible stage of the electron transport along the transmembrane chain of cofactors-carriers. Some natural factors are revealed that significantly increase the rates of efficient processes in these stages. The influence on their efficiency by the “bottleneck” in the energy migration chain is established. The overall quantum yield of photosynthesis in these stages is determined.     

中国市域自然保护区时空格局演变特征

市域是自然保护地整合优化的具体实施单元,揭示市域自然保护区的时空格局演变特征和发展趋势对于全面建设中国特色自然保护地体系具有重要的现实意义。基于1975-2020年间隔5 a的10期中国自然保护区名录,通过标准差椭圆、探索性空间数据分析、空间Markov链等方法首次从市域尺度探讨了中国自然保护区的时空动态演变特征和发展趋势,并引入地学信息图谱构建其变化模式。结果表明:①中国市域自然保护区比例呈现先快速增长后逐步稳定的趋势,但整体仍以低值区为主。市域间自然保护区比例差异明显,逐步形成了“西高东低”的空间格局。重心整体表现为“东南-西北-东北”的迁移过程,重心移动距离和速度逐渐缩小。1990年以后,中国市域自然保护区比例始终呈现“东北-西南”向的空间分布格局。②市域自然保护区比例存在显著的空间相关性且相关性总体呈上升之势,但空间集聚水平的变化逐年缩小。局部空间异质性较强,高高和低低集聚区均呈扩张态势。③从变化模式图谱来看,中国市域自然保护区比例等级以“前期变化-低频-逐步提升”(ESC-LQ-T1)的方式进行,主要图谱类型为“空缺区转为低值区”。市域自然保护区比例等级类型转移具有稳定性和显著的时空异质性,后期出现了“俱乐部收敛”现象,且邻域背景在其动态变化过程中发挥着重要作用。未来要加强对空缺区和低值区市域单元的自然资源进行摸底评估,将其最有价值的区域纳入自然保护区,同时推广区域联合保护机制,促进自然保护地体系高质量建设。     

Conditional Markov Chain Design for Accrual Clinical Trials

Randomization in a comparative experiment has, as one aim, the control of bias in the initial selection of experimental units. When the experiment is a clinical trial employing the accrual of patients, two additional aims are the control of admission bias and control of chronologic bias. This can be accomplished by using a method of randomization, such as the “biased coin design” of Efron, which sequentially forces balance. As an extension of Efron's design, this paper develops a class of conditional Markov chain designs. The detailed randomization employed utilizes the sequential imbalances in the treatment allocation as states in a Markov process. Through the use of appropriate transition probabilities, a range of possible designs can be attained. An additional objective of physical randomization is to provide a model for data analysis. Such a randomization theoretic analysis is presented for the current designs. In addition, Monte Carlo sampling results are given to support the proposed normal theory approximation to the exact randomization distribution.     

A search for patterns in the nucleotide sequence of the MS2 genome

Summary The nucleotide sequence of the RNA of the bacteriophage MS2 was examined by computer for internal patterns. We used a technique which analyzes a nucleotide sequence as a Markov chain. This led us to discover patterns within the translated and untranslated regions of the RNA in addition to those patterns formed by the codons. One of the more surprising results of this analysis was the discovery that the non-coding sequences in the genome are as highly ordered, although in a different sense, as the genes themselves. Also of interest was the discovery that the codon frequency distributions for the three genes are similar.     

Hierarchical Bayesian modeling of spatially correlated health service outcome and utilization rates

We present Bayesian hierarchical spatial models for spatially correlated small-area health service outcome and utilization rates, with a particular emphasis on the estimation of both measured and unmeasured or unknown covariate effects. This Bayesian hierarchical model framework enables simultaneous modeling of fixed covariate effects and random residual effects. The random effects are modeled via Bayesian prior specifications reflecting spatial heterogeneity globally and relative homogeneity among neighboring areas. The model inference is implemented using Markov chain Monte Carlo methods. Specifically, a hybrid Markov chain Monte Carlo algorithm (Neal, 1995, Bayesian Learning for Neural Networks; Gustafson, MacNab, and Wen, 2003, Statistics and Computing, to appear) is used for posterior sampling of the random effects. To illustrate relevant problems, methods, and techniques, we present an analysis of regional variation in intraventricular hemorrhage incidence rates among neonatal intensive care unit patients across Canada.     

Spatio-temporal variation in Markov chain models of subtidal community succession

In this paper we ask whether succession in a rocky subtidal community varies in space and time, and if so how much affect that variation has on predictions of community dynamics and structure. We describe succession by Markov chain models based on observed frequencies of species replacements. We use loglinear analysis to detect and quantify spatio‐temporal variation in the transition matrices describing succession. The analysis shows that space and time, but not their interaction, have highly significant effects on transition probabilities. To explore the ecological importance of the spatio‐temporal variability detected in this analysis, we compare the equilibria and the transient dynamics among three Markov chain models: a time‐averaged model that includes the effects of space on succession, a spatially averaged model that include the effects of time, and a constant matrix that averages over the effects of space and time. All three models predicted similar equilibrium composition and similar rates of convergence to equilibrium, as measured by the damping ratio or the subdominant Lyapunov exponent. The predicted equilibria from all three models were very similar to the observed community structure. Thus, although spatial and temporal variation is statistically significant, at least in this system this variation does not prevent homogeneous models from predicting community structure.     

Bias explorer: measurements of compositional bias in EMBL and GenBank sequence files

A Windows application for compositional analysis of sequenced genomes (EMBL or GenBank flat files) is available as freeware. The application allows the user to quantify word bias using Markov chain analysis and it allows the user to generate sliding window data for GC-skew, AT-skew, purine excess, keto excess and discrete word counts. The mathematical routines reside in a dynamic link library (DLL), which can be used independently by other applications. The software is available for download at http://www.dfuni.dk/~anfu/Bioinformatics/Main.htm. This revised version was published online in June 2006 with corrections to the Cover Date.     

Identifying the spatial effects and driving factors of urban PM2.5 pollution in China

Exploring exactly where air pollution comes from, and identifying the key factors that influence it, can provide a scientific basis for the rational formulation and effective implementation of air pollution policies in China. Based on the data from 2001 to 2012 covering PM_2.5 concentrations in 285 Chinese cities, we use dynamic spatial panel models to empirically analyze the key driving factors of this air pollution. Results show that China’s urban smog demonstrates both obvious global spatial autocorrelation and local spatial agglomeration. There is a significant inverted “U-shaped” curve between economic development level and air pollution, and most cities are in the phase in which pollution is increasing in conjunction with improvements to the economy. Due to a rapid increase in population in built up areas, a high-proportion of secondary industry, a coal-dominated energy structure and increasing traffic intensity, China’s smog problem is becoming more and more serious. FDI probably will not play a future role in mitigating the air pollution. Central heating in winter in northern China further aggravates local smog to a certain extent. Because China’s haze pollution presents path-dependent characteristics and spatial spillover effects in the time dimension and in the space dimension respectively, so smog alleviation policies should be implemented based both on the strategies of maximizing effort and regional joint prevention and control.     

中国农业环境效率时空演变及其驱动因素

农业环境效率是衡量经济与资源环境协调发展的重要指标,研究农业环境效率对实现农业资源的高效率利用具有重要意义。基于2004-2019年中国30个省（市、区）面板数据,运用SBM （Slacks-based model）模型、空间马尔科夫链、GIS技术和空间计量模型,分析了农业环境效率的时空演变特征,研究了农业环境效率变动的驱动因素。结果表明：（1）2004-2019年,考虑非期望产出的农业环境效率要低于不考虑非期望产出情况,呈持续上升趋势;区域差异整体呈倒"V "型变化趋势,东西方向上递减,南北方向上呈" U"型趋势特征。（2）中国不同等级的农业环境效率空间差异明显,地理空间格局在空间演变过程具有重要作用,空间关联性特征显著,不同类型的演变存在显著的时空惯性。（3）中国农业环境效率存在空间溢出效应,农业经济水平、环境规制、农业生产能力是提升农业环境效率的关键因素,财政支农水平、工业化水平、农作物种植结构、农业机械强度则是制约效率提升的约束要素。     

生态网络中物质、能量流动的时间链分析

本文以Ｍａｒｋｏｖ过程理论为基础,利用转移矩阵对生态网络中物质、能量流动和随机行为进行的描述;将输入的物质、能量在生态网络中宏观分布随时间的变化定义为物质、能量流动的时间链,并给一般生态网络中物质、能量流动时间链的分析方法。两个稳态生态网络中物质、能量流动的时间链分析表明,时间链直观地反映了物质、能量在流动中流失或耗散的宏观行为,由于物质再循机普遍存在,使得物质流动的时间链与能量流动的时间链有着质     

Variable context Markov chains for HIV protease cleavage site prediction

Deciphering the knowledge of HIV protease specificity and developing computational tools for detecting its cleavage sites in protein polypeptide chain are very desirable for designing efficient and specific chemical inhibitors to prevent acquired immunodeficiency syndrome. In this study, we developed a generative model based on a generalization of variable order Markov chains (VOMC) for peptide sequences and adapted the model for prediction of their cleavability by certain proteases. The new method, called variable context Markov chains (VCMC), attempts to identify the context equivalence based on the evolutionary similarities between individual amino acids. It was applied for HIV-1 protease cleavage site prediction problem and shown to outperform existing methods in terms of prediction accuracy on a common dataset. In general, the method is a promising tool for prediction of cleavage sites of all proteases and encouraged to be used for any kind of peptide classification problem as well.     

福建省生态环境与高质量发展耦合关系及驱动机制

研究生态环境与高质量发展耦合关系时空演变及驱动机制,对实现可持续发展、构建生态文明体系和建设美丽中国具有重要意义。综合构建了生态环境-高质量发展指标体系,运用耦合协调度模型、空间马尔科夫链和BP神经网络-地理加权回归组合模型,系统探讨2005-2020年福建省生态环境与高质量发展耦合关系的时空演变及驱动机制。结果表明：（1）研究期间,福建省生态环境指数整体稳定,而高质量发展指数呈现先降低后上升的变化趋势,二者在空间上的组合关系整体呈现错位特征。（2）福建省县域生态环境与高质量发展指标之间的耦合关系呈先降低后上升的趋势,且呈现明显的空间异质性。（3）协调发展和绿色发展是驱动生态环境质量提高的关键因素,其影响程度呈"圈层式"空间分布特征。     

碳排放影响下的中国省域土地利用效率差异动态变化与影响因素

土地利用效率是衡量区域经济社会系统运行质量的重要参数。在构建了考虑非期望产出的土地利用效率评价指标体系基础之上,综合运用Super SBM-undesirable DEA和多元统计等研究方法,分析了2003—2016年碳排放影响下的中国省域土地利用效率的时空特征及其影响因素。研究结果表明:中国区域土地利用效率处于中低水平,碳排放非期望产出降低了15%的土地利用效率水平;与全国发展水平空间格局一致,土地利用效率省际差异在空间上呈现出自东向西逐渐递减的分异特征;2003—2016年,土地利用效率演化呈现出"U"型演进特征,区域差异呈现收敛态势;不考虑非期望产出的土地利用效率水平呈现下降态势;非期望产出和能源消费投入过大成为土地利用效率提升的主要限制因素。经济发展水平、对外开放程度以及固定资产投资对土地利用效率的提升起到了显著的正向作用;现阶段城镇化水平的增加对土地利用效率的提升具有微弱促进作用,且仅在东部地区具有显著性。基于松弛变量冗余度和影响因素分析,针对不同区域土地利用效率低效的差异性因素,提出了相应的对策措施。