Suchergebnisse

To encourage firms to engage in green production, two different types of investment funding, namely external funds from agencies outside the supply chain (e.g., government subsidy), and internal funds from supply chain partners (e.g., greening cost-sharing with the retailer), are investigated in this paper. Based on game theory, the decision-making behavior and profits of a competitive supply chain consisting of a green manufacturer, a regular manufacturer, and a retailer are analyzed under both funding schemes. The results show that while both government subsidy and greening cost-sharing contract can achieve the goals of increasing a product&rsquo ; s degree of greenness and increasing the sales of green products, there are differences between these two methods in reaching these goals. Further, both via theoretical and numerical analysis, we find that although both the green manufacturer and the retailer can greatly benefit from government subsidy and greening cost-sharing contract, they may have different preferences regarding these two methods, which are mainly related to the size of the government subsidy, the fraction of greening cost-sharing with the retailer, the Research and Development (R& ; D) cost coefficient, the greenness sensitivity coefficient, and price sensitivity coefficient. Finally, the supply chain members&rsquo ; behaviors (including the production and pricing decisions and, the choice of funds investment) are largely affected by the government subsidy mechanism.

Due to the country's rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, R2. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated R2 of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city.

Fil: Tenorio, Rosangela. University of Nottingham Ningbo; China. ; Fil: Yang, Tong. Middlesex University; Reino Unido. ; Fil: Oro, Bruno. University of Nottingham Ningbo; China. ; Gansú 'Plug-in' Studio was a community outreach project based in China developed over a course of 2 years (2014-2016). It proposed a complementary approach of education to improve quality of learning in primary schools in rural areas. The premise for the work was to engage higher education institutions such as UNNC and primary education institutions, such as HEPAN, by exchange of resources, (e.g. physical or human - staff and students as well as contexts as real opportunities for learning). It worked using design as a tool for development and environmental awareness. It worked in partnership with industry and government institutions and it involved over 120 students and 15 staff from 5 different areas (Architecture, Product Design, Environmental Engineering, Film Studies and English/Education). Through interactive workshops with academics and industry practitioners, UNNC students were guided to realise their ideas through innovative design and sustainable engineering solutions, also developed their communication and team working skills for real to help rural education and community development.

Gansú 'Plug-in' Studio was a community outreach project based in China developed over a course of 2 years (2014-2016). It proposed a complementary approach of education to improve quality of learning in primary schools in rural areas. The premise for the work was to engage higher education institutions such as UNNC and primary education institutions, such as HEPAN, by exchange of resources, (e.g. physical or human - staff and students as well as contexts as real opportunities for learning). It worked using design as a tool for development and environmental awareness. It worked in partnership with industry and government institutions and it involved over 120 students and 15 staff from 5 different areas (Architecture, Product Design, Environmental Engineering, Film Studies and English/Education). Through interactive workshops with academics and industry practitioners, UNNC students were guided to realise their ideas through innovative design and sustainable engineering solutions, also developed their communication and team working skills for real to help rural education and community development. Meanwhile, pupils and teachers in rural areas could develop sufficiently as their urban counterparts through this connected learning hub. The purpose of this paper is to discuss the processes and outcomes of Gansu Studio. The Plug-in Gansu Unit was designed to be the first net positive energy rural school building in China producing an estimated 18304 kWh/year of renewable energy to the school and reverting back to the county grid an annual extra clean energy 2304kWh/annum upon completion. An estimated 22 tons of water per year would be also saved.