Volume 9, Issue 1

While blockchain technology is commonly considered potentially disruptive in various regards, there is a lack of understanding where and how blockchain technology is effectively applicable and where it has remarkable practical effects [1] . Against this background, we present and discuss a case study at length on the impact of this technology in the concrete setting of small short-term loans in retail banking. We propose to banks a robust and scalable blockchain technology with proof of stake and limited energy consumption used to streamline their processes, resulting in lower transaction and administration costs. This is made possible by smart contracts. Thereby, we facilitate small scale lending at high frequencies and short-term duration as well as an easier and more efficient way to connect small borrowers and lenders.

Big data analytics (BDA) in e-commerce, which is an emerging field that started in 2006, deeply affects the development of global e-commerce, especially its layout and performance in the U.S. and China. This paper seeks to examine the relative influence of theoretical research of BDA in e-commerce to explain the differences between the U.S. and China by adopting a statistical analysis method on the basis of samples collected from two main literature databases, Web of Science and CNKI, aimed at the U.S. and China. The results of this study help clarify doubts regarding the development of China’s e-commerce, which exceeds that of the U.S. today, in view of the theoretical comparison of BDA in e-commerce between them.

The construction industry is one of the largest energy consumers in China. It not only uses energy directly but also consumes a large amount of embodied energy hidden in intermediate goods and services from other industries. This paper utilizes the multi-regional input-output (MRIO) model to measure the embodied energy consumption in China’s construction industry at the province level. To quantify the backward and forward linkages between the construction industry and other industries, the energy-driven and energy-driving abilities of the construction industry are investigated for 30 provinces. The results show that the values of the energy-driven coefficients are significantly larger than the energy-driving coefficients, indicating that the construction industry is highly dependent on products from other industries and other regions, not vice versa. The construction industries in the southwest and northwest regions of China have the highest energy-driven coefficients, showing that the construction industries in west China heavily rely on indirect energy embodied in goods and services. This particularly applies in Guangxi and Hainan provinces, which have the highest energy-driven abilities and the lowest energy-driving abilities. The findings can facilitate the policy-makers to examine the critical energy-saving paths.

With the continuous development of economy, the bottleneck problem of environmental resources has become increasingly prominent. Enterprise environmental governance technology innovation incentive has become an important issue for the development of government, society and enterprises. Under the control of enterprise’s expected economic target, this paper discusses the synergistic incentive effect of environmental policy and green finance on enterprise’s environmental governance technology innovation decision by using nonlinear programming model. The results show that when the funds for environmental governance technological innovation are insufficient, there is an optimal decision space to use green financial loans to implement technological innovation and upgrade, and then achieve the expected economic goals; Under a given level of environmental governance technology, environmental policies affect whether enterprises can make decisions on technological innovation and upgrading of environmental governance; Green financial mechanism will not. However, when the enterprise makes the decision of environmental governance technology upgrading, it will affect the enterprise’s decision on green financial loan amount. The results of the study have guiding significance for the formulation of environmental policy and green financial policy, as well as the decision-making of enterprise environmental governance technology innovation and upgrading.

Scholars have a variety of theoretical explanations for housing price growth. However, few scholars have studied the internal influence mechanism among urbanization, land finance, and housing price. Based on the data of 182 prefecture-level cities from 2009 to 2016, this paper studies the influence of land finance on housing price under different urbanization rate levels. The study finds that with the increase of urbanization rate, the effect of land finance on housing price presents a “U” shape. Specifically, an increase in land finance by 1% results in a corresponding increase in average housing price by 0.18%, with relatively low urbanization rate, 0.06% with medium level of urbanization rate, and 0.38% with high level of urbanization rate.

In the era of big data, correlation analysis is significant because it can quickly detect the correlation between factors. And then, it has been received much attention. Due to the good properties of generality and equitability of the maximal information coefficient (MIC), MIC is a hotspot in the research of correlation analysis. However, if the original approximate algorithm of MIC is directly applied into mining correlations in big data, the computation time is very long. Then the theoretical time complexity of the original approximate algorithm is analyzed in depth and the time complexity is n 2.4 when parameters are default. And the experiments show that the large number of candidate partitions of random relationships results in long computation time. The analysis is a good preparation for the next step work of designing new fast algorithms.