OWNERSHIP AND INDUSTRIAL POLLUTION CONTROL: EVIDENCE FROM CHINA

This study explores the differences in pollution control performances of industrial enterprises with various ownerships in China - State owned (SOE), collectively or community owned (COE), privately owned (POE), foreign directly invested (FDI) companies as well as joint ventures. A survey was conducted of approximately 1000 industrial firms in three provinces in China, which collects the detailed firm-level information in the year of 1999. Personal interviews of enterprises managers were also conducted in these samples, and subjective information was collected. Analyses have been performed on the differences in receiving and reacting to environmental regulatory enforcement, community pressure, environmental services, and internal environmental management among different ownerships. The determinants of the industrial pollution emissions in China are identified in the econometrical analyses. The results show that FDI and COE have better environmental performances, while SOEs and the POEs in China are the worst.Environmental Economics and Policy,

The impact of environmental performance rating and disclosure: an empirical analysis of perceptions by polluting firms'managers in China

Environmental performance rating and disclosure has emerged as a substitute or complement for traditional pollution regulation, especially in developing countries. Using data from China's Green Watch program, this study extends previous research on performance rating and disclosure by considering firms'perceptions of public and market responses to their ratings. The results suggest that the Green Watch has significantly increased market and stakeholder pressures on managers to improve their firms’ environmental performance. More specifically, controlling for the characteristics of locations, firms, and individual managers, the analysis finds that firms with better ratings perceive positive impacts on market competitiveness, overall market value, and relationships with different stakeholders, while the firms with bad ratings are more likely to perceive deterioration. Among these factors, managers perceive a more active role for markets than for stakeholder relations.Markets and Market Access,Microfinance,Water and Industry,Brown Issues and Health,Green Issues

Clustering, Classification, and Factor Analysis in High Dimensional Data Analysis

Clustering, classification, and factor analysis are three popular data mining techniques. In this dissertation, we investigate these methods in high dimensional data analysis. Since there are much more features than the sample sizes and most of the features are non-informative in high dimensional data, dimension reduction is necessary before clustering or classification can be made. In the first part of this dissertation, we reinvestigate an existing clustering procedure, optimal discriminant clustering (ODC; Zhang and Dai, 2009), and propose to use cross-validation to select the tuning parameter. Then we develop a variation of ODC, sparse optimal discriminant clustering (SODC) for high dimensional data, by adding a group-lasso type of penalty to ODC. We also demonstrate that both ODC and SDOC can be used as a dimension reduction tool for data visualization in cluster analysis. In the second part, three existing sparse principal component analysis (SPCA) methods, Lasso-PCA (L-PCA), Alternative Lasso PCA (AL-PCA), and sparse principal component analysis by choice of norm (SPCABP) are applied to a real data set the International HapMap Project for AIM selection to genome-wide SNP data, the classification accuracy is compared for them and it is demonstrated that SPCABP outperforms the other two SPCA methods. Third, we propose a novel method called sparse factor analysis by projection (SFABP) based on SPCABP, and propose to use cross-validation method for the selection of the tuning parameter and the number of factors. Our simulation studies show that SFABP has better performance than the unpenalyzed factor analysis when they are applied to classification problems

Environmental performance rating and disclosure : an empirical investigation of China's green watch program

Environmental performance rating and disclosure has emerged as an alternative or complementary approach to conventional pollution regulation, especially in developing countries. However, little systematic research has been conducted on the effectiveness of this emerging policy instrument. This paper investigates the impact of a Chinese performance rating and disclosure program, Green Watch, which has been operating for 10 years. To assess the impact of Green Watch, the authors use panel data on pollution emissions from rated and unrated firms, before and after implementation of the program. Controlling for the characteristics of firms and locations, time trend, and initial level of environmental performance, the analysis finds that firms covered by Green Watch improve their environmental performance more than non-covered firms. Bad performers improve more than good performers, and moderately non-compliant firms improve more than firms that are significantly out of compliance. The reasons for these different responses seem to be that the strengths of incentives that the disclosure program provides to the polluters at different levels of compliance are different and the abatement costs of achieving desired levels of ratings are different for different firms.Water and Industry,Brown Issues and Health,Green Issues,Pollution Management&Control,Energy Production and Transportation

Lattice-Based Group Signatures: Achieving Full Dynamicity (and Deniability) with Ease

In this work, we provide the first lattice-based group signature that offers full dynamicity (i.e., users have the flexibility in joining and leaving the group), and thus, resolve a prominent open problem posed by previous works. Moreover, we achieve this non-trivial feat in a relatively simple manner. Starting with Libert et al.'s fully static construction (Eurocrypt 2016) - which is arguably the most efficient lattice-based group signature to date, we introduce simple-but-insightful tweaks that allow to upgrade it directly into the fully dynamic setting. More startlingly, our scheme even produces slightly shorter signatures than the former, thanks to an adaptation of a technique proposed by Ling et al. (PKC 2013), allowing to prove inequalities in zero-knowledge. Our design approach consists of upgrading Libert et al.'s static construction (EUROCRYPT 2016) - which is arguably the most efficient lattice-based group signature to date - into the fully dynamic setting. Somewhat surprisingly, our scheme produces slightly shorter signatures than the former, thanks to a new technique for proving inequality in zero-knowledge without relying on any inequality check. The scheme satisfies the strong security requirements of Bootle et al.'s model (ACNS 2016), under the Short Integer Solution (SIS) and the Learning With Errors (LWE) assumptions. Furthermore, we demonstrate how to equip the obtained group signature scheme with the deniability functionality in a simple way. This attractive functionality, put forward by Ishida et al. (CANS 2016), enables the tracing authority to provide an evidence that a given user is not the owner of a signature in question. In the process, we design a zero-knowledge protocol for proving that a given LWE ciphertext does not decrypt to a particular message

On the LURWC property of Orlicz sequence space

Necessary and sufficient conditions for \textbf{URWC} points and \textbf{LURWC} property are given in Orlicz sequence space $l_M$