Search results

This article uses the "Green Finance Reform and Innovation Pilot Zone" promulgated in 2017 as an example to construct a quasi-natural experiment and uses the difference-in-difference method to test the impact of the implementation of the "Green Finance Reform and Innovation Pilot Zone" on the green innovation activities. It was found that the policy promotes the quantity and quality of corporate green innovation. The mechanism test showed that policy promotes the R&D investment and expands the credit scale. The study further found that green finance policies enhance the green innovation of enterprises as government environmental regulation is strengthened. Finally, green innovation by state-owned enterprises is more strongly promoted in the pilot green finance reform and innovation zones, and green innovation by enterprises in non-polluting sectors is more sensitive to the policy, with a heterogeneous pattern of policy effects in eastern and non-eastern China. Therefore, green finance policies should be promoted to achieve an effective combination of financial resource allocation and corporate green innovation to promote the construction of ecological civilization.

AbstractIn recent years, e‐commerce has become a phenomenal symbol for the ongoing socio‐economic transformation in China. Driven by the growth of the Internet and cyberspace, e‐commerce changes both the traditional patterns of economic transactions and the social relations embedded in social activities. E‐commerce is a new catalyst for economic growth in China as it reduces restrictions to market access and the costs of transaction. This research examines the geography of e‐commerce in China within a theoretical framework grounded by social theory and neoclassical economics. After presenting a background of the development of e‐commerce in China, the spatiality of e‐commerce and its correlation to socio‐economic variables are examined with both quantitative and geovisualization techniques. Results of this research address that the growth of e‐commerce in China presents a nationwide inequality constrained by local economic, politic, and infrastructure conditions. The growth of e‐commerce in China presents a hybrid feature in terms of spatiality, which relies on structures in both cyberspace and physical space. Policy implications and suggestions for future research were suggested based on these empirical results. A discussion of limitations associated with this research and a concluding note close the research.

Cap 1. Importanza nutrizionale e dietetica dell'olio extra vergine di oliva 1.1 La definizione e la classificazione dell'olio di oliva 1.2 Importanza nutrizionale e dietetica dell'olio extra vergine di oliva Cap2. Il settore mondiale dell'olio di oliva e il settore italiano 2.1 La descrizione del settore mondiale dell'olio di oliva 2.11 La produzione mondiale 2.12Il consumo mondiale 2.2 Descrizione dell'olio di oliva nel settore italiano Fase agricola: 2.21 Le azienda olivicole 2.211 Collocazione orografica 2.212 La forma giuridica 2.213 La superficie e la produzione 2.214 Oli DOP, IGP e l'olio extravergine di oliva biologico 2.22 I frantoi 2.23 Le imprese che confezionano e vendono l'olio Fase commerciale 2.24 La vendita dell'olio di oliva nel 2013. 2.25 I prezzi degli oli di oliva 2.27 Le esportazioni degli oli italiani di oliva nel 2013 Cap3. L'olio extravergine di oliva nel mercato cinese 3.1 I fattori favorevoli che stimolano la domanda dell'olio 3.2 l'analisi PESI 3.21 Il fattore politico 3.22. Il fattore economico 3.23il fattore sociale 3.24il fattore tecnico 3.3 Market 's Competitive Enviroment 3.31Concorrenti diretti 3.32Potenziali entranti 3.33Produttori di beni sostitutivi 3.34Clienti 3.35Fornitori 3.4L'analisi del comportamento dei consumatori cinesi 3.41 La conoscenza dei cinesi sull'olio di oliva 3.42 L'abitutine d'acquisto 3.43 Il luogo di acquisto e la comunicazione 3.5 Marketing mix dell'olio di oliva 3.51 prodotto 3.52 prezzo 3.53 canale 3.54 promozione Cap4. Storia dello sviluppo dell'olio extravergine d'oliva in Cina e la conclusione. L'allegato di un questionario

This thesis consists of three essays on empirical asset pricing around three themes: evaluating linear factor asset pricing models by comparing their misspecified measures, understanding the long-run risk on consumption-leisure to investigate their pricing performances on cross-sectional returns, and evaluating conditional asset pricing models by using the methodology of dynamic cross-sectional regressions. The first chapter is ``Comparing Asset Pricing Models: What does the Hansen-Jagannathan Distance Tell Us?''. It compares the relative performance of some important linear asset pricing models based on the Hansen-Jagannathan (HJ) distance using data over a long sample period from 1952-2011 based on U.S. market. The main results are as follows: first, among return-based linear models, the Fama-French (1993) five-factor model performs best in terms of the normalized pricing errors, compared with the other candidates. On the other hand, the macro-factor model of Chen, Roll, and Ross (1986) five-factor is not able to explain industry portfolios: its performance is even worse than that of the classical CAPM. Second, the Yogo (2006) non-durable and durable consumption model is the least misspecified, among consumption-based asset pricing models, in capturing the spread in industry and size portfolios. Third, the Lettau and Ludvigson (2002) scaled consumption-based CAPM (C-CAPM) model obtains the smallest normalized pricing errors pricing gross and excess returns on size portfolios, respectively, while Santos and Veronesi (2006) scaled C-CAPM model does better in explain the return spread on portfolios of U.S. government bonds. The second chapter (``Leisure, Consumption and Long Run Risk: An Empirical Evaluation'') uses a long-run risk model with non-separable leisure and consumption, and studies its ability to price equity returns on a variety of portfolios of U.S. stocks using data from 1948-2011. It builds on early work by Eichenbaum et al. (1988) that explores the empirical properties of intertemporal asset ...

Industrial green technology innovation has become an important content in achieving high-quality economic growth and comprehensively practicing the new development concept in the new era. This paper measures the efficiency of industrial green technology innovation and regional differences based on Chinese provincial panel data from 2005 to 2018, using a combination of the super efficiency slacks-based measure (SBM) model for considering undesirable outputs and the Dagum Gini coefficient method, and discusses and analyses the factors influencing industrial green technology innovation efficiency by constructing a spatial econometric model. The results show that: firstly, industrial green technology innovation efficiency in China shows a relatively stable development trend, going through three stages: "stationary period", "recession period" and "growth period". However, the efficiency gap between different regions is obvious, specifically in the eastern > central > western regions of China, and the industrial green technology efficiency innovation in the central and western regions is lower than the national average. Secondly, regional differences in the efficiency of industrial green technology innovation in China are evident but tend to narrow overall, with the main reason for the overall difference being regional differences. In terms of intra-regional variation, variation within the eastern region is relatively stable, variation within the central region is relatively low and shows an inverted 'U' shaped trend, and variation within the western region is high and shows a fluctuating downward trend. Thirdly, the firm size, government support, openness to the outside world, environmental regulations and education levels contribute to the efficiency of industrial green technology innovation. In addition, the industrial structure hinders the efficiency of industrial green technology innovation, and each influencing factor has different degrees of spatial spillover effects.

In this paper, new statistical algorithms for accurate peak detection in the metabolomic data are proposed. Specifically, liquid chromatograph‐mass spectrometry data are analyzed. The discretized skew‐t
mixture model for peak detection is proposed. It shows great flexibility and capability in fitting skewed or heavy‐tailed peaks. The methodology is further extended to cross‐sample peak alignment for identifying the true peaks. A measure of peak credibility is provided through the assessment of misclassification probabilities between two cross‐sample peaks. The proposed algorithms are applied to spike‐in data with promising results.