Exploring the Complexity of Location Choices of the Creative Class in Europe: Evidence from the EU Labor Force Survey 1995-2010

This paper proposes a new idea for the current argument over Florida's cultural policies, as location choices of the creative class is a complex process involving some basic aspects of socio-economic progress. Based on the European Labor Force Survey (EU LFE) dataset, tolerance and openness indicators which represent the quality of a "people climate" are found to be positively correlated with the creative class’s location in large regions and less so in smaller ones, where business climate-related parameters, i.e., the quality of local governments and the location of universities, have stronger positive effects on locational choices of the creative class. Moreover, graduates with non-creative jobs and creative professionals (i.e., workers who provide creative solutions during the work process such as high-tech technicians or legal and healthcare workers) are concerned more about the people climate, while creative workers with a degree and a creative core (e.g., workers who provide original ideas such as scientists, engineers and artists) are more likely to prioritize a business climate. Therefore, we argue that the promotion of a "tolerant" climate, as Florida advocates, is not a one-size-fits-all solution. Instead, policy makers should appropriately relate different preferences of creative workers to their unique strengths. This provides more insights into defining the concept of creativity beyond prioritized individual success, as well as understanding the preferences and actual needs of highly skilled workers in Europe

Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

Click-Through Rate prediction is an important task in recommender systems, which aims to estimate the probability of a user to click on a given item. Recently, many deep models have been proposed to learn low-order and high-order feature interactions from original features. However, since useful interactions are always sparse, it is difficult for DNN to learn them effectively under a large number of parameters. In real scenarios, artificial features are able to improve the performance of deep models (such as Wide & Deep Learning), but feature engineering is expensive and requires domain knowledge, making it impractical in different scenarios. Therefore, it is necessary to augment feature space automatically. In this paper, We propose a novel Feature Generation by Convolutional Neural Network (FGCNN) model with two components: Feature Generation and Deep Classifier. Feature Generation leverages the strength of CNN to generate local patterns and recombine them to generate new features. Deep Classifier adopts the structure of IPNN to learn interactions from the augmented feature space. Experimental results on three large-scale datasets show that FGCNN significantly outperforms nine state-of-the-art models. Moreover, when applying some state-of-the-art models as Deep Classifier, better performance is always achieved, showing the great compatibility of our FGCNN model. This work explores a novel direction for CTR predictions: it is quite useful to reduce the learning difficulties of DNN by automatically identifying important features

Driving factors of carbon emissions from household energy combustion in China

Reducing carbon emissions resulting from household direct energy combustion while ensuring equal access to energy is essential for fair transition towards carbon neutrality. In this regard, understanding the driving factors of household direct carbon emissions and projecting future emission pathways are necessary for effective policy implementation. In this study, we applied the logarithmic mean Divisia index model to investigate changes in household direct carbon emissions from 2000 to 2021, and established six scenarios to assess the impacts of energy efficiency improvement and energy transition on carbon reduction. The results showed that the growing household expenditure continuously drives the increase in direct carbon emissions, while the decline in the energy demand per unit household expenditure and energy transition drives the decrease in carbon emissions. Replacing direct energy combustion with electricity is vital to reduce household direct emissions. This study highlights the importance of improving the energy efficiency and promoting the electrification of household energy consumption. Policy interventions should be implemented to facilitate behavioural changes, technology development, and low-carbon infrastructure construction

The Polarizing Trend of Regional CO2 Emissions in China and Its Implications

CO2 emissions are unevenly distributed both globally and regionally within nation-states. Given China's entrance into the new stage of economic development, an updated study on the largest CO2 emitter's domestic emission distribution is needed for effective and coordinated global CO2 mitigation planning. We discovered that domestic CO2 emissions in China are increasingly polarized for the 2007-2017 period. Specifically, the domestically exported CO2 emissions from the less developed and more polluting northwest region to the rest of China has drastically increased from 165 Mt in 2007 to 230 Mt in 2017. We attribute the polarizing trend to the simultaneous industrial upgrading of all regions and the persistent disparity in the development and emission decoupling of China's regions. We also noted that CO2 emissions exported from China to the rest of the world has decreased by 41% from 2007 to 2017, with other developing countries filling up the vacancy. As this trend is set to intensify, we intend to send an alarm message to policy makers to devise and initiate actions and avoid the continuation of pollution migration

Institutional trading in volatile markets : evidence from Chinese stock markets

We investigate the daily stock returns of all A-shares listed on the Shanghai and Shenzhen stock exchanges over the period 2010-2017. Using daily cash flow data on the largest category of trades by value, we construct a proxy for high-value institutional trading activity. We demonstrate that high-value institutional transactions consistently exacerbate firm-level abnormal stock returns on extreme market movement days. We then highlight the conflating influence of regulator imposed daily limits on firm-level stock price movements and conclude that binding price limits act to exacerbate the destabilising effects associated with high-value institutional trades in Chinese stock markets

The idiosyncratic risk in Chinese stock market

Using daily stock return data of all listed firms in Chinese stock market from 1998 to 2018, we disaggregate the volatility of common stocks at the market, industry and firm levels. We find market volatility, on average, is the highest while firm volatility tends to lead to market and industry volatility series. None long-term trend time series behaviour exists for all three volatility series and firm volatility is best described by an autoregressive process with regime shifts associated with the structural market reforms or volatile market movements. We further proceed to identify the source of volatility at the industry level and find the idiosyncratic volatility in the largest manufacturing industry not only accounts for the largest proportion in the aggregate firm volatility but also is the lead indicator for the idiosyncratic volatility of other industries. Finally, unlike Brandt et. al. [Review of Financial Studies 23(2): 863-899 (2010)], we find the idiosyncratic volatility in Chinese stock market is associated with high stock trading activities by institutional investors, the result of which is also robust when using other measures of idiosyncratic volatility