Municipal infrastructure is a fundamental facility for the normal operation and development of an urban city and is of significance for the stable progress of sustainable urbanization around the world, especially in developing countries. Based on the municipal infrastructure data of the prefecture-level cities in China, municipal infrastructure development is assessed comprehensively using a FA (factor analysis) model, and then the stochastic model STIRPAT (stochastic impacts by regression on population, affluence and technology) is examined to investigate key factors that influence municipal infrastructure of cities in various stages of urbanization and economy. This study indicates that the municipal infrastructure development in urban China demonstrates typical characteristics of regional differentiation, in line with the economic development pattern. Municipal infrastructure development in cities is primarily influenced by income, industrialization and investment. For China and similar developing countries under transformation, national public investment remains the primary driving force of economy as well as the key influencing factor of municipal infrastructure. Contribution from urbanization and the relative consumption level, and the tertiary industry is still scanty, which is a crux issue for many developing countries under transformation. With economic growth and the transformation requirements, the influence of the conventional factors such as public investment and industrialization on municipal infrastructure development would be expected to decline, meanwhile, other factors like the consumption and tertiary industry driven model and the innovation society can become key contributors to municipal infrastructure sustainability.

Residential carbon dioxide emissions can be divided into a direct component caused by consumers via direct energy usage and an indirect component caused by consumers buying and using products to meet their needs, with a higher proportion caused by the latter. Based on Beijing panel data for 1993–2012, an economic boom period in China, indirect carbon dioxide emissions were separately calculated for urban and rural households using the consumer lifestyle approach (CLA) model. Then, an extended stochastic impact by regression on population, affluence, and technology (STIRPAT) model was used to analyze the influence from two aspects, social economy, and land use, with high precision. Results indicate that indirect CO2 emissions in Beijing households display a rising trend in urban areas but a slight decrease in rural areas. Technology influences and forest land are, respectively, the most important aspects of the social economy and land use. Higher population and urbanization resulted in enhanced emissions in both urban and rural areas. The Engel coefficient presented a negative correlation with indirect CO2 emissions for both rural and urban areas. Compared with urban areas, the per capita net income of rural areas restrained consumption. The consumption structure of urban residents was more biased toward the tertiary industry than that of rural residents. Although technical progress has proceeded, it cannot offset urban residents’ indirect CO2 emissions caused by the large amount and rapid growth of consumption. Regarding land use, urban construction land net primary productivity (NPP) was high and not an important factor contributing to indirect CO2 emissions. Forest and lawn primarily served a recreational function and exhibited a positive impact. Water and cultivated land offered insufficient production and thus had a negative influence. For rural residents, lawn and cultivated land production is self-sufficient. Forests offer a carbon sequence effect, and construction land expansion increased the proportion of developed area, offering a scale effect that resulted in reduced carbon emissions. Based on the results, alternative carbon emission reduction policies have been proposed for each tested influence aspect to reduce emissions, including policies for optimizing industrialization quality, constructing a medium-density city, increasing space efficiency, encouraging sustainable consumption behavior, and increasing the efficiency of energy utilization.

Abstract:Outdoor lighting is becoming increasingly widespread, and residents are suffering from serious light pollution as a result. Residents' awareness of their rights to protection has gradually increased. However, due to the sometimes-inaccessible nature of residential vertical light incidence intensity data and the high cost of obtaining specific measurements, there is no appropriate hierarchic compensation for residents suffering from different degrees of light pollution. It is therefore important to measure light pollution levels and their damage at the neighborhood scale to provide residents with basic materials for proper protection and to create more politically-suitable solutions. This article presents a light pollution assessment method that is easy to perform, is low-cost and has a short data-processing cycle. This method can be used to monitor residential zone light pollution in other cities. We chose three open areas to test the spatial variation pattern of light intensity. The results are in accordance with spatial interpolation patterns and can be fit, with high precision, using the inverse distance weighted interpolation (IDW) method. This approach can also be used in three dimensions to quantitatively evaluate the distribution of light intensity. We use a mixed-use zone in Beijing known as The Place as our case study area. The vertical illumination at the windows of residential buildings ranges from 2 lux to 23 lux; the illumination in some areas is far higher than the value recommended by CIE. Such severe light pollution can seriously interfere with people's daily lives and has a serious influence on their rest and health. The results of this survey will serve as an important database to assess whether the planning of night-time lighting is scientific, and it will help protect the rights of residents and establish distinguished compensation mechanisms for light pollution.