中国省际环境污染的动态综合评价及影响因素

基于单一污染物难以表达环境污染状况的不足,本文提出了能够代表整体环境状况的污染排放指数,并首次运用一种基于整体差异的动态客观综合评价方法对中国28个省1995～2006年的环境污染状况进行了测度,分析了中国和分区域污染排放指数的变动趋势,并基于经济结构和能源因素视角,采用面板数据模型检验了中国和分区域污染排放指数的影响因素。分析表明,要实现十一五规划中主要污染物在2010年比2006年减少10%的约束性目标,从三大区域来看,应该重点监控东部地区;从省级区域看,应该重点加大对河北、江苏、辽宁、山东、四川、河南、山西、广东、浙江、湖北、湖南等省份污染排放的治理力度,提高能源利用效率、制定符合市场机制的能源价格体系、加大贸易开放度和降低煤炭消费在能源消费中的比例可以减少污染排放。     

Inventory investment and financial constraints in the Italian manufacturing industry: A panel data GMM approach

Three large unbalanced panels of Italian manufacturing firms observed over the period 1991–2009 are employed to assess, by means of a dynamic GMM approach, whether the existence of financial frictions is suitable to explain deviations of inventories from their long-run path. A negative response of inventory investment to the presence of financial burdens might provide evidence of a significant role played by the financial framework in conditioning the real side of the economy, especially during recession years, when liquidity problems arise. The negative effect is found over the entire analyzed period, with firms' dimensional aspects accounting more than risk characteristics to explain the phenomenon, but the inclusion of recessionary dummies into the model leads to controversial and puzzling results. A significant recessionary effect is found during the Nineties, accounting for inventories being more sensitive to financial frictions during the main recessionary peaks, 1993 and 1996. The result is not confirmed by the most recent estimates, especially the ones referring to the 2008–2009 recessionary shock, whose effects are investigated for the first time by a paper addressing the inventory investment–financial constraints subject. Alternative hypothesis for the proposed results have been tested on data. Firms were found to rely on inventory decumulation to a lesser extent compared to the past, to generate internal financing. More specifically, disinvestments in financial assets were found to represent, as a matter of fact, one of the main drivers adopted to ease liquidity tensions: a negative and strongly significant relationship with inventory investment was detected, after controlling for short-run liquidity constraints at firm level. By contrast, only a weak negative relationship was established with fixed capital during the same recessionary biennium.     

Consistent inference in fixed-effects stochastic frontier models

The classical stochastic frontier panel data models provide no mechanism to disentangle individual time invariant unobserved heterogeneity from inefficiency. Greene (2005a, b) proposed the so-called “true” fixed-effects specification that distinguishes these two latent components. However, due to the incidental parameters problem, his maximum likelihood estimator may lead to biased variance estimates. We propose two alternative estimators that achieve consistency for $n\to \infty$ with fixed $T$. Furthermore, we extend the Chen et al. (2014) results providing a feasible estimator when the inefficiency is heteroskedastic and follows a first-order autoregressive process. We investigate the behavior of the proposed estimators through Monte Carlo simulations showing good finite sample properties, especially in small samples. An application to hospitals’ technical efficiency illustrates the usefulness of the new approach.     

Nonparametric fixed effects model for panel data with locally stationary regressors

We develop methods for inference in nonparametric time-varying fixed effects panel data models that allow for locally stationary regressors and for the time series length $T$ and cross-section size $N$ both being large. We first develop a pooled nonparametric profile least squares dummy variable approach to estimate the nonparametric function, and establish the optimal convergence rate and asymptotic normality of the resultant estimator. We then propose a test statistic to check whether the bivariate nonparametric function is time-varying or the time effect is separable, and derive the asymptotic distribution of the proposed test statistic. We present several simulated examples and two real data analyses to illustrate the finite sample performance of the proposed methods.     

A gossip based information fusion protocol for distributed frequent itemset mining

The computational complexity, huge memory space requirement, and time-consuming nature of frequent pattern mining process are the most important motivations for distribution and parallelization of this mining process. On the other hand, the emergence of distributed computational and operational environments, which causes the production and maintenance of data on different distributed data sources, makes the parallelization and distribution of the knowledge discovery process inevitable. In this paper, a gossip based distributed itemset mining (GDIM) algorithm is proposed to extract frequent itemsets, which are special types of frequent patterns, in a wireless sensor network environment. In this algorithm, local frequent itemsets of each sensor are extracted using a bit-wise horizontal approach (LHPM) from the nodes which are clustered using a leach-based protocol. Heads of clusters exploit a gossip based protocol in order to communicate each other to find the patterns which their global support is equal to or more than the specified support threshold. Experimental results show that the proposed algorithm outperforms the best existing gossip based algorithm in term of execution time.     

iSDS: a self-configurable software-defined storage system for enterprise

ABSTRACT

Storage is one of the most important aspects of IT infrastructure for various enterprises. But, enterprises are interested in more than just data storage; they are interested in such things as more reliable data protection, higher performance and reduced resource consumption. Traditional enterprise-grade storage satisfies these requirements at high cost. It is because traditional enterprise-grade storage is usually designed and constructed by customised field-programmable gate array to achieve high-end functionality. However, in this ever-changing environment, enterprises request storage with more flexible deployment and at lower cost. Moreover, the rise of new application fields, such as social media, big data, video streaming service etc., makes operational tasks for administrators more complex. In this article, a new storage system called intelligent software-defined storage (iSDS), based on software-defined storage, is described. More specifically, this approach advocates using software to replace features provided by traditional customised chips. To alleviate the management burden, it also advocates applying machine learning to automatically configure storage to meet dynamic requirements of workloads running on storage. This article focuses on the analysis feature of iSDS cluster by detailing its architecture and design.     

Semiparametric time series regression modeling with a diverging number of parameters

Variable selection and error structure determination of a partially linear model with time series errors are important issues. In this paper, we investigate the regression coefficient and autoregressive order shrinkage and selection via the smoothly clipped absolute deviation penalty for a partially linear model with a divergent number of covariates and finite order autoregressive time series errors. Both consistency and asymptotic normality of the proposed penalized estimators are derived. The oracle property of the resultant estimators is proved. Simulation studies are carried out to assess the finite‐sample performance of the proposed procedure. A real data analysis is made to illustrate the usefulness of the proposed procedure as well.     

中国区域碳排放空间计量研究

如何从空间视角实现经济发展与碳减排双赢,是建设“美国中国”的重要推手,也是生态文明建设的必然要求。基于STIRPAT模型,从区域层面构建碳排放驱动因素扩展STIRPAT模型,并运用空间杜宾模型实证考察各驱动因素对碳排放规模和碳排放强度的影响。结果显示：地区间碳排放存在显著的示范和带头作用,驱动因素通过直接和间接途径影响碳排放,除能源价格外,其他影响因素均表现出显著性。因此,实现碳减排需要充分考虑空间相关性、异质性和外溢性,稳步推进城镇化进程,加大技术创新步伐,优化产业结构升级和能源消费结构,适度提高能源价格,在扩大对外开放的同时加大对外商投资的甄别。