A New Marketing Channel Management Strategy Based on Frequent Subtree Mining

For most manufacturers, success or failure is determined by how effectively and efficiently their products are sold through their marketing channel members, so the management of marketing channels plays an important role in market competition. Most existing work studies the problem of marketing channel management in a qualitative way. Recently, with the increase of amount of sales data, how to enhance the marketing channel quantitatively is significant. As the marketing channel can be viewed as a tree, in this paper, a new marketing channel management strategy based on frequent subtree mining is proposed. The proposed method is illustrated under the real-world sales data in ERDOS group. Firstly, the tree transaction is formed monthly. For each monthly transaction, only those channel members that pass the basic sales plan will be included. Secondly, we use the TreeMiner algorithm to discover embedded frequent subtrees. Finally, different management strategies are used for different kinds of discovered patterns. We show that our method can correspond to the seven decision areas in traditional marketing channel management

Data Mining In Oil Price Time Series Analysis

This paper sums up the applications of statistic models such as ARCH-family models, cointegration theory and Granger causality etc in oil price time series analysis and introduces the method of data mining combined with statistic knowledge to analysis oil price time series. In addition, the paper also explains advantages, functions, relevant technologies of this method and its potential applications in hedging the oil shock risk

CO₂ emission reduction potential in China from combined effects of structural adjustment of economy and efficiency improvement

China has committed to decreasing its emission intensity by 60% to 65% by 2030 compared to 2005 levels and achieving carbon neutrality by 2060. It is of great importance to evaluate the CO2 emission reduction potential to quantify the amount of CO2 emissions that can be less generated and the amount that should be balanced out. Economic structure adjustment and CO2 emission efficiency improvement will contribute to mitigating CO2 emissions, which always happen simultaneously in the real world. However, few studies consider these issues simultaneously, which can lead to inaccurate estimation. A scenario analysis framework is proposed to estimate their combined effects, and an indicator is proposed to measure the technical feasibility of achieving the reduction potential. A set of scenarios are designed based on this framework and we find that: (1) to achieve carbon neutrality, 6161.16 Mt of CO2 emissions of China can be less generated compared to 2017 levels by significantly increasing its tertiary industry share to high-income entities’ level and adopting the most advanced technology to improve emission efficiency; the remaining 2732.40 Mt of CO2 emissions should be removed by carbon offsetting. Regarding emission intensity, 81.39% can be reduced compared with the 2005 level; and (2) Technical feasibility analysis shows Sichuan, Chongqing, and Anhui have the largest technical barriers in achieving the reduction potential. The proposed scenario analysis framework can provide a reference not only for China to achieve the emission mitigation pledges, but for countries with significant technological differences and structure adjustment to formulate mitigation strategies.</p

The governance-production nexus of eco-efficiency in Chinese resource-based cities:A two-stage network DEA approach

For decades, resource-based cities in China have significantly contributed to China's socio-economic development. The heavy resource dependence of resource-based cities inevitably leads to a series of environmental problems. Mitigating environmental impacts in an unthinking manner might be disruptive for economic development. Improving eco-efficiency has been a crucial solution for protecting the environment while mitigating its negative economic impact. However, the method commonly used to evaluate the eco-efficiency – that is, the black-box data envelopment analysis (DEA) – cannot examine the inefficiencies of the internal structure, and as a result, the underlying management defects are unclear. To open the black box, this study presents a two-stage network DEA framework incorporating government and industrial sectors and measures the eco-efficiency of 84 resource-based cities during the post-financial crisis period (2007–2015). The results indicate that the average eco-efficiency of China's resource-based cities shows a promising increase, and there is a positive relationship between governance efficiency and production efficiency. The decreasing trend of governance efficiency in the Central, Western, and Northeast regions after 2014 shows the low quality of the government sector in the usage of fiscal income. Proactive disclosure of how the government sector conducts public business and spends taxpayers' money should be made to increase transparency, attract more entrepreneurial resources to carry out production activities, and further improve sustainability. The two-stage network DEA framework helps obtain more insights into the internal management defects of the government and industrial sectors and enhance their cooperation to improve the eco-efficiency precisely

Comparisons of CO₂ emission performance between secondary and service industries in Yangtze River Delta cities

To put the brakes on global climate change, China, the world's top emitter, has established ambitious CO2 emissions reduction targets. Industry-level emissions analysis can help policymakers determine better ways to achieve mitigation targets. This study is the first to target the total-factor carbon emission performance (TCPI) of secondary and service industries. We first compile industry-level CO2 emission inventories of 25 Yangtze River Delta cities during 2007–2016. The TCPI of secondary and service industries is then estimated by the non-radial directional distance function. We then compare the TCPI of the two industries across levels, dynamics, and inequalities using a global metafrontier approach. The results show the TCPI of the service industry (0.563 in 2016) was significantly higher than that of secondary industry (0.256 in 2016), suggesting that the service industry was more carbon-friendly. The TCPI gap between the secondary and service industries narrowed over the study period. The TCPI of secondary industry showed a promising increase during 2007–2016 with an annual growth rate of 2.30%, reflecting the positive effects of the government's reforms and environmental regulations. By contrast, the service industry saw a downward trend in TCPI, decreasing by 1.68% annually, primarily because it is a newcomer to low-carbon development. TCPI inequality in secondary industry was much larger than in the service industry, suggesting that significant heterogeneity exists in secondary industry. Therefore, policymakers should implement targeted mitigation policies for secondary industry, and place decarbonising the service industry on the agenda to reverse its decreasing TCPI

Assessing the economic impacts of a perfect storm of extreme weather, pandemic control, and export restrictions: A methodological construct

This article investigates the economic impacts of a multi-disaster mix comprising extreme weather, such as flooding, pandemic control, and export restrictions, dubbed a "perfect storm." We develop a compound-hazard impact model that improves on the ARIO model by considering the economic interplay between different types of hazardous events. The model considers simultaneously cross-regional substitution and production specialization, which can influence the resilience of the economy to multiple shocks. We build scenarios to investigate economic impacts when a flood and a pandemic lockdown collide and how these are affected by the timing, duration, and intensity/strictness of each shock. In addition, we examine how export restrictions during a pandemic impact the economic losses and recovery, especially when there is the specialization of production of key sectors. The results suggest that an immediate, stricter but shorter pandemic control policy would help to reduce the economic costs inflicted by a perfect storm, and regional or global cooperation is needed to address the spillover effects of such compound events, especially in the context of the risks from deglobalization

Entropy-based Chinese city-level MRIO table framework

Cities are pivotal hubs of socioeconomic activities, and consumption in cities contributes to global environmental pressures. Compiling city-level multi-regional input-output (MRIO) tables is challenging due to the scarcity of city-level data. Here we propose an entropy-based framework to construct city-level MRIO tables. We demonstrate the new construction method and present an analysis of the carbon footprint of cities in China's Hebei province. A sensitivity analysis is conducted by introducing a weight reflecting the heterogeneity between city and province data, as an important source of uncertainty is the degree to which cities and provinces have an identical ratio of intermediate demand to total demand. We compare consumption-based emissions generated from the new MRIO to results of the MRIO based on individual city input-output tables. The findings reveal a large discrepancy in consumption-based emissions between the two MRIO tables but this is due to conflicting benchmark data used in the two tables

Emission accounting and drivers in East African countries

East Africa is typical of the less developed economies that have emerged since the 21st century, whose brilliant economic miracle has also triggered the rapid growth of energy consumption and carbon dioxide emissions. However, previous carbon accounting studies have never focused on the region. Based on multi-source data, this paper rebuilt the 45-sectors carbon emission inventories of eight East African countries from 2000 to 2017, and used index decomposition analysis to quantify the drivers of growth. Here we found that overall the CO2 emissions show a 'two-stage exponential growth' pattern, with significant heterogeneity between countries. In terms of the energy mix, technical progress in hydro and geothermal energy was almost offset by a growing appetite for oil and coal, making it the weak and valuable factor driving emissions reduction (−1.4Mt). But it was far from enough to overcome the pressure of economic and population growth, which brought about a 13Mt and 11Mt emission growth respectively from 2000 to 2017. Increasing energy intensity due to industrialization and transport development also contributed to an increment of 6.4Mt. Low-carbon policies should be tailored to local conditions and targeted at the improvement of energy efficiency and use of renewable energy so as to achieve a win-win situation between sustainable economic growth and emission reduction

FDTD Modelling of Silver Nanoparticles Embedded in Phase Separation Interface of H-PDLC

We report localized surface plasmon resonance (LSPR) of silver nanoparticles (NPs) embedded in interface of phase separation of holographic polymer-dispersed liquid crystal (H-PDLC) gratings using Finite-Difference Time Domain method. We show that silver NPs exhibit double resonance peak at the interface, and these peaks are influenced by the angle of incident light. We observe a blue shift of the wavelength of resonance peak as the incident angle increases. However, the location of silver NPs at the interface has nearly no effect on the wavelength of resonance peak. Also we show near-field and far-field properties surrounding silver NPs and find that field distribution can be controlled through rotation of incident angle. Therefore, LSPR properties of silver NPs within H-PDLC gratings can be excited by appropriate wavelength and angle of the incident light