Impacts of FDI Renewable Energy Technology Spillover on China's Energy Industry Performance

Environmental friendly renewable energy plays an indispensable role in energy industry development. Foreign direct investment (FDI) in advanced renewable energy technology spillover is promising to improve technological capability and promote China’s energy industry performance growth. In this paper, the impacts of FDI renewable energy technology spillover on China’s energy industry performance are analyzed based on theoretical and empirical studies. Firstly, three hypotheses are proposed to illustrate the relationships between FDI renewable energy technology spillover and three energy industry performances including economic, environmental, and innovative performances. To verify the hypotheses, techniques including factor analysis and data envelopment analysis (DEA) are employed to quantify the FDI renewable energy technology spillover and the energy industry performance of China, respectively. Furthermore, a panel data regression model is proposed to measure the impacts of FDI renewable energy technology spillover on China’s energy industry performance. Finally, energy industries of 30 different provinces in China based on the yearbook data from 2005 to 2011 are comparatively analyzed for evaluating the impacts through the empirical research. The results demonstrate that FDI renewable energy technology spillover has positive impacts on China’s energy industry performance. It can also be found that the technology spillover effects are more obvious in economic and technological developed regions. Finally, four suggestions are provided to enhance energy industry performance and promote renewable energy technology spillover in China.

Building performance evaluation of domestic energy efficient retrofits in current and future climates

Approximately half of the houses in Northern Ireland were built before any form of minimum thermal specification or energy efficiency standard was enforced. Furthermore, 44% of households are categorised as being in fuel poverty; spending more than 10% of the household income to heat the house to bring it to an acceptable level of thermal comfort. To bring existing housing stock up to an acceptable standard, retrofitting for improving the energy efficiency is essential and it is also necessary to study the effectiveness of such improvements in future climate scenarios. This paper presents the results from a year-long performance monitoring of two houses that have undergone retrofits to improve energy efficiency. Using wireless sensor technology internal temperature, humidity, external weather, household gas and electricity usage were monitored for a year. Simulations using IES-VE dynamic building modelling software were calibrated using the monitoring data to ASHARE Guideline 14 standards. The energy performance and the internal environment of the houses were then assessed for current and future climate scenarios and the results show that there is a need for a holistic balanced strategy for retrofitting.

Development of sustainable, innovative and energy-efficient concrete, based on the integration of all-waste materials: SUS-CON panels for building applications

The building sector requires the worldwide production of 4 billion tonnes of cement annually, consuming more than 40% of global energy and accounting for about 8% of the total CO2 emissions. The SUS-CON project aimed at integrating waste materials in the production cycle of concrete, for both ready-mixed and pre-cast applications, resulting in an innovative light-weight, ecocompatible and cost-effective construction material, made by all-waste materials and characterized by enhanced thermal insulation performance and low embodied energy and CO2. Alkali activated “cementless” binders, which have recently emerged as eco-friendly construction materials, were used in conjunction with lightweight recycled aggregates to produce sustainable concrete for a range of applications. This paper presents some results from the development of a concrete made with a geopolymeric binder (alkali activated fly ash) and aggregate from recycled mixed plastic. Mix optimisation was achieved through an extensive investigation on production parameters for binder and aggregate. The mix recipe was developed for achieving the required fresh and hardened properties. The optimised mix gave compressive strength of about 7 MPa, flexural strength of about 1.3 MPa and a thermal conductivity of 0.34 W/mK. Fresh and hardened properties were deemed suitable for the industrial production of precast products. Precast panels were designed and produced for the construction of demonstration buildings. Mock-ups of about 2.5 x 2.5 x 2.5 m were built at a demo park in Spain both with SUS-CON and Portland cement concrete, monitoring internal and external temperatures. Field results indicate that the SUS-CON mock-ups have better insulation. During the warmest period of the day, the measured temperature in the SUS-CON mock-ups was lower.

Advanced Business Simulations-Incorporating business and process execution data

Key Performance Indicators (KPIs) and their predictions are widely used by the enterprises for informed decision making. Nevertheless , a very important factor, which is generally overlooked, is that the top level strategic KPIs are actually driven by the operational level business processes. These two domains are, however, mostly segregated and analysed in silos with different Business Intelligence solutions. In this paper, we are proposing an approach for advanced Business Simulations, which converges the two domains by utilising process execution & business data, and concepts from Business Dynamics (BD) and Business Ontologies, to promote better system understanding and detailed KPI predictions. Our approach incorporates the automated creation of Causal Loop Diagrams, thus empowering the analyst to critically examine the complex dependencies hidden in the massive amounts of available enterprise data. We have further evaluated our proposed approach in the context of a retail use-case that involved verification of the automatically generated causal models by a domain expert.