Assessing the design of three carbon trading pilot programs in China

To help overcome the challenge of growing CO₂ emissions, China is experimenting with market-based instruments, including pilot CO₂ emissions trading systems (ETSs) in seven regions that serve as precursors of a national CO₂ ETS. Implementing an ETS in a rapidly growing economy in which government authorities exercise significant control over markets poses many challenges. This study assesses how well three of the most developed pilot ETSs, in Guangdong, Shanghai, and Shenzhen, have adapted carbon emissions trading to China's economic and political context. We base our study on new information gathered through interviews with local pilot ETS regulators and experts, analysis of recent trading data, and extensive legal and literature reviews. We point out instances in which pilot regulators have deftly tailored carbon emissions trading to China's unique context and instances in which designs are insufficient to ensure smooth operation. We also indicate areas in which broader institutional reforms of China's political economy may be required for carbon emissions trading to operate successfully. We make nine recommendations to improve the design and operation of the pilot programs and to inform the construction of a national CO₂ ETS.     

Analysis on the carbon trading approach in promoting sustainable buildings in China

With the high growth urbanization and increasing new urban population, the huge demand for infrastructures and dwellings has become a great challenge for the sustainable development in Chinese cities. The building sector shares one fourth of total energy consumption in the country and plays an important role in reducing the energy consumption and the consequential green house gas (GHG) emissions. Some policies have been issued for promoting the low carbon sustainable development in China's buildings. However, existing barriers especially the investment barriers substantially prevent the low carbon technologies and service from being employed effectively. The carbon trading scheme of cap-and-trade is now widely accepted as one cost-effective way to deal with the climate change issue in the world, and it can be utilized for overcoming the barriers to carbon reduction activities in China's building sector. A new Clean Development Mechanism (CDM) energy performance based method is designed for reducing transaction costs in implementing CDM projects in China's buildings before 2020. And then a “step by step” approach is formed to establish the domestic and international carbon trading mechanism to effectively reduce GHG missions in China's building sector after 2020.     

Optimal portfolio trading subject to stochastic dominance constraints under second‐order autoregressive price dynamics

This paper studies the optimal portfolio trading problem under the generalized second‐order autoregressive execution price model. The problem of minimizing expected execution cost under the proposed price model is formulated as a quadratic programming (QP) problem. For a risk‐averse trader, problem formulation under the second‐order stochastic dominance constraints results in a quadratically constrained QP problem. Under some conditions on the execution price model, it is proved that the portfolio trading problems for risk‐neutral and risk‐averse traders become convex programming problems, which have many theoretical and computational advantages over the general class of optimization problems. Extensive numerical illustrations are provided, which render the practical significance of the proposed execution price model and the portfolio trading problems.     

“一带一路”沿线国家粮食虚拟水贸易网络研究

“一带一路”沿线国家是世界粮食重要产区，有效利用虚拟水贸易战略，有利于缓解沿线国家的粮食困境，实现水资源的高效配置。论文基于2010—2016年的生产和气候数据，测算了沿线65个国家7种粮食的单位虚拟水含量，并结合相应粮食的贸易数据，构建虚拟水贸易网络模型。研究发现：该贸易网络具有小世界特性，出现逆全球化征兆；俄罗斯、乌克兰等是该贸易网络中主要的粮食虚拟水净出口国家，埃及、伊朗等是主要的粮食虚拟水净进口国家；中国保持着较高的中心性，其粮食虚拟水贸易规模持续扩大，在该网络中起着重要的桥梁作用。研究结果为改善“一带一路”沿线国家的农产品生产与贸易结构、合理利用水资源提供了可视化依据。     

Ensemble learning of rule-based evolutionary algorithm using multi-layer perceptron for supporting decisions in stock trading problems

Classification is a major research field in pattern recognition and many methods have been proposed to enhance the generalization ability of classification. Ensemble learning is one of the methods which enhance the classification ability by creating several classifiers and making decisions by combining their classification results. On the other hand, when we consider stock trading problems, trends of the markets are very important to decide to buy and sell stocks. In this case, the combinations of trading rules that can adapt to various kinds of trends are effective to judge the good timing of buying and selling. Therefore, in this paper, to enhance the performance of the stock trading system, ensemble learning mechanism of rule-based evolutionary algorithm using multi-layer perceptron (MLP) is proposed, where several rule pools for stock trading are created by rule-based evolutionary algorithm, and effective rule pools are adaptively selected by MLP and the selected rule pools cooperatively make decisions of stock trading. In the simulations, it is clarified that the proposed method shows higher profits or lower losses than the method without ensemble learning and buy&hold.     

Understanding volatility dynamics in the EU-ETS market

We study the short-term price behavior of Phase 2 EU emission allowances. We model returns and volatility dynamics, and we demonstrate that a standard ARMAX-GARCH framework is inadequate for this modeling and that the gaussianity assumption is rejected due to a number of outliers. To improve the fitness of the model, we combine the underlying price process with an additive stochastic jump process. We improve the model's performance by introducing a time-varying jump probability that is explained by two variables: the daily relative change in the volume of transactions and the European Commission's announcements regarding the supply of permits. We show that (i) sharp increases in volume have led to increased volatility during the April 2005–December 2007 period but not for the period beginning in January 2008, and (ii) announcements induce jumps in the process that tend to increase volatility across both periods. Thus, authorities face a trade off between disseminating information effectively and promoting market stability.     

Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review

Despite the wide application of evolutionary computation (EC) techniques to rule discovery in stock algorithmic trading (AT), a comprehensive literature review on this topic is unavailable. Therefore, this paper aims to provide the first systematic literature review on the state-of-the-art application of EC techniques for rule discovery in stock AT. Out of 650 articles published before 2013 (inclusive), 51 relevant articles from 24 journals were confirmed. These papers were reviewed and grouped into three analytical method categories (fundamental analysis, technical analysis, and blending analysis) and three EC technique categories (evolutionary algorithm, swarm intelligence, and hybrid EC techniques). A significant bias toward the applications of genetic algorithm-based (GA) and genetic programming-based (GP) techniques in technical trading rule discovery is observed. Other EC techniques and fundamental analysis lack sufficient study. Furthermore, we summarize the information on the evaluation scheme of selected papers and particularly analyze the researches which compare their models with buy and hold strategy (B&H). We observe an interesting phenomenon where most of the existing techniques perform effectively in the downtrend and poorly in the uptrend, and considering the distribution of research in the classification framework, we suggest that this phenomenon can be attributed to the inclination of factor selections and problem in transaction cost selections. We also observe the significant influence of the transaction cost change on the margins of excess return. Other influenced factors are also presented in detail. The absence of ways for market trend prediction and the selection of transaction cost are two major limitations of the studies reviewed. In addition, the combination of trading rule discovery techniques and portfolio selection is a major research gap. Our review reveals the research focus and gaps in applying EC techniques for rule discovery in stock AT and suggests a roadmap for future research.     

The European Union Emission Trading System and technological change: The case of the Italian pulp and paper industry

We evaluate the contribution of technological change in reducing CO₂ emissions in the Italian pulp and paper industry during the first and second phases of application of the European Union Emission Trading System (EU-ETS). We decompose the variation in emission and emission intensity into three different types of effects: a composition effect, a technique effect and a scale effect. The composition effect measures the change in emissions and emissions intensity due to a shift in production towards products that cause less emissions. The technique effect measures the change per each type of product, thereby accounting for technology improvements in the production of each type of good produced. The scale effect singles out the reduction in total emission due to an overall reduction in output. We show that the first phase of the application of EU-ETS has led to a reduction in both emissions and emission intensity due to the composition effect. The technological change has had a limited negative impact on emissions in the first phase, while in the second phase there has been limited technology improvement in the industry. However, the figures of the scale effect show that the larger reduction in emission is due to the overall decrease in output.     

Potential cost savings from internal/external CO2 emissions trading in the Korean electric power industry

Korea plans to introduce an emissions trading scheme for the controlling greenhouse gas emissions in 2015. Using Shephard's (1970) output distance function, we first estimate the shadow price of CO₂ for power generators in the Korean fossil-fueled electric generation industry. Then, by assuming that each power generator is required to reduce CO₂ emissions by one ton, we compute the potential cost savings from internal trading among generators within the same plant and from external trading across plants at prevailing market prices. The results indicate that, on average, the generators paid $14.63 to abate one ton of CO₂ emissions in 2007. Plants realized additional gains through external trading. In particular, cost savings from trades between different fuel-fired plants were substantial.     

A Takagi–Sugeno fuzzy model combined with a support vector regression for stock trading forecasting

The turning points prediction scheme for future time series analysis based on past and present information is widely employed in the field of financial applications. In this research, a novel approach to identify turning points of the trading signal using a fuzzy rule-based model is presented. The Takagi–Sugeno fuzzy rule-based model (the TS model) can accurately identify daily stock trading from sets of technical indicators according to the trading signals learned by a support vector regression (SVR) technique. In addition, when new trading points are created, the structure and parameters of the TS model are constantly inherited and updated. To verify the effectiveness of the proposed TS fuzzy rule-based modeling approach, we have acquired the stock trading data in the US stock market. The TS fuzzy approach with dynamic threshold control is compared with a conventional linear regression model and artificial neural networks. Our result indicates that the TS fuzzy model not only yields more profit than other approaches but also enables stable dynamic identification of the complexities of the stock forecasting system.