Xiamen Key Laboratory of Urban Metabolism

Xiamen, China

Xiamen Key Laboratory of Urban Metabolism

Xiamen, China
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Zhao J.,Chinese Institute of Urban Environment | Zhao J.,Xiamen Key Laboratory of Urban Metabolism | Chen S.,Nanjing Institute of Environmental Sciences | Jiang B.,CAS Research Center for Eco Environmental Sciences | And 6 more authors.
Science of the Total Environment | Year: 2013

Irrespective of which side is taken in the densification-sprawl debate, insights into the relationship between urban green space coverage and urbanization have been recognized as essential for guiding sustainable urban development. However, knowledge of the relationships between socio-economic variables of urbanization and long-term green space change is still limited. In this paper, using simple regression, hierarchical partitioning and multi-regression, the temporal trend in green space coverage and its relationship with urbanization were investigated using data from 286 cities between 1989 and 2009, covering all provinces in mainland China with the exception of Tibet. We found that: [1] average green space coverage of cities investigated increased steadily from 17.0% in 1989 to 37.3% in 2009; [2] cities with higher recent green space coverage also had relatively higher green space coverage historically; [3] cities in the same region exhibited similar long-term trends in green space coverage; [4] eight of the nine variables characterizing urbanization showed a significant positive linear relationship with green space coverage, with 'per capita GDP' having the highest independent contribution (24.2%); [5] among the climatic and geographic factors investigated, only mean elevation showed a significant effect; and [6] using the seven largest contributing individual factors, a linear model to predict variance in green space coverage was constructed. Here, we demonstrated that green space coverage in built-up areas tended to reflect the effects of urbanization rather than those of climatic or geographic factors. Quantification of the urbanization effects and the characteristics of green space development in China may provide a valuable reference for research into the processes of urban sprawl and its relationship with green space change. © 2012 Elsevier B.V.

Yang D.,Chinese Institute of Urban Environment | Yang D.,Xiamen Key Laboratory of Urban Metabolism | Kao W.T.M.,Chinese Institute of Urban Environment | Kao W.T.M.,Xiamen Key Laboratory of Urban Metabolism | And 3 more authors.
Ecological Modelling | Year: 2014

The need to create sustainable cities has led to increasing concern on achieving healthy spatial metabolic interactions and system sustainability. Based on emergy synthesis and an urban spatial conceptual framework, we employ a set of seven emergy-based indicators to evaluate the sustainability of spatiotemporal metabolism for Xiamen, southeast China, using 1987-2007 land use and socio-economic statistic data. The results show a general improvement in socio-economic performance (emergy intensity, GDP emergy ratio, and emergy turnover ratio), but a steady deterioration in environmental performance (emergy self-support ratio, emergy density, and waste density) during the period 1987-2007. An increasing environmental and socio-economic metabolic gap exists between the built-up urban sprawl region (USR) and urban footprint regions (UFRs) due to resource privation and environmental space occupation, potentially undermining system sustainability. Compared to other Chinese cities and provinces, Xiamen still exhibited relatively weaker sustainability in 2002 due to increasing pressure on ecosystem health. Environment-oriented, society-oriented and cross-boundary-oriented metabolic strategies should be incorporated into future city development to foster urban system sustainability. © 2013 .

Huang Y.,Jimei University | Li F.,University of Chinese Academy of Sciences | Li F.,CAS Institute of Geographical Sciences and Natural Resources Research | Bai X.,Australian National University | And 2 more authors.
Environmental Science and Policy | Year: 2012

Coastal regions in China are undergoing rapid land use change, but little attention is paid to the implications of this change to local community. Assessment of vulnerability of coastal community to land use change is an important step for enhancing the understanding and decision-making to reduce such vulnerability. This article presents an analytical framework and associated indicator system to assess and compare vulnerability of communities to land use change in coastal areas, and present a case study in China applying this framework. The study includes quantification of Exposure Index (EI), Sensitivity Index (SI) and Adaptive Capacity Index (AI). EI is to measure intensity of land use. SI and AI are based on some socio-economic attributes of the native residents, as well as their view on environmental change and management. Based on the quantification of SI and AI, Vulnerability Index (VI) can be assessed and compared among different communities. This framework was applied in a case study in Maluan Bay, Xiamen, China. The area consists of four administrative, as rural communities in the 1980s, evolving into four distinctive communities with different policies and development modes. Comparison of EI and VI reveals large disparity among communities. Analysis demonstrated that vulnerability was not evenly distributed across communities, which might be linked to the different stage of transformation the community was undergoing. For the case areas, vulnerability tends to increase with the increase of exposure to land use change, but can peak off once the community start to benefit socio-economically from development. The most vulnerable community is the one where native residents lost their livelihood, but benefited a little from economic development. This may suggest the need for tailor-made policy responses to help them to benefit from development and aid their smooth integration into the city, only in this way can enhance adaptive capacity of coastal communities to use change of land and sea. © 2012 Elsevier Ltd.

Xu L.,Chinese Institute of Urban Environment | Xu L.,Xiamen Key Laboratory of Urban Metabolism | Gao P.,Xiamen City Appearance and Environmental Sanitation Management Office | Cui S.,Chinese Institute of Urban Environment | And 2 more authors.
Waste Management | Year: 2013

Accurate forecasting of municipal solid waste (MSW) generation is crucial and fundamental for the planning, operation and optimization of any MSW management system. Comprehensive information on waste generation for month-scale, medium-term and long-term time scales is especially needed, considering the necessity of MSW management upgrade facing many developing countries. Several existing models are available but of little use in forecasting MSW generation at multiple time scales. The goal of this study is to propose a hybrid model that combines the seasonal autoregressive integrated moving average (SARIMA) model and grey system theory to forecast MSW generation at multiple time scales without needing to consider other variables such as demographics and socioeconomic factors. To demonstrate its applicability, a case study of Xiamen City, China was performed. Results show that the model is robust enough to fit and forecast seasonal and annual dynamics of MSW generation at month-scale, medium- and long-term time scales with the desired accuracy. In the month-scale, MSW generation in Xiamen City will peak at 132.2 thousand tonnes in July 2015 - 1.5. times the volume in July 2010. In the medium term, annual MSW generation will increase to 1518.1 thousand tonnes by 2015 at an average growth rate of 10%. In the long term, a large volume of MSW will be output annually and will increase to 2486.3 thousand tonnes by 2020 - 2.5. times the value for 2010. The hybrid model proposed in this paper can enable decision makers to develop integrated policies and measures for waste management over the long term. © 2013 Elsevier Ltd.

Lin J.,Chinese Institute of Urban Environment | Lin J.,Xiamen Key Laboratory of Urban Metabolism | Liu Y.,Chinese Institute of Urban Environment | Liu Y.,Xiamen Key Laboratory of Urban Metabolism | And 5 more authors.
Energy Policy | Year: 2013

For more holistic inventory estimation, this paper uses a hybrid approach to access the carbon footprint of Xiamen City in 2009. Besides carbon emissions from the end-use sector activities (called Scope 1+2 by WRI/WBCSD) in normal research, carbon emissions from the cross-boundary traffic and the embodied energy of key urban imported materials (namely Scope 3) were also included. The results are as follow: (1) Carbon emissions within Scope 1+2 only take up 66.14% of total carbon footprint, while emissions within Scope 3 which have usually been ignored account for 33.84%. (2) Industry is the most carbon-intensive end use sector which contributes 32.74% of the total carbon footprint and 55.13% of energy use emissions in Scope 1+2. (3) The per capita carbon footprint of Xiamen is just about one-third of that in Denver. (4) Comparing with Denver, the proportion of embodied emissions in Xiamen was 10.60% higher than Denver. Overall, Xiamen is relatively a low-carbon city with characters of industrial carbon-intensive and high embodied emissions. Further analysis indicates that the urbanization and industrialization in Xiamen might cause more material consumption and industrial emissions. These highlight the importance of management for Scope 3 emissions in the developing cities. © 2013 Elsevier Ltd.

Lin T.,Chinese Institute of Urban Environment | Lin T.,Xiamen Key Laboratory of Urban Metabolism | Yu Y.,Chinese Institute of Urban Environment | Yu Y.,Xiamen Key Laboratory of Urban Metabolism | And 5 more authors.
PLoS ONE | Year: 2013

Devising policies for a low carbon city requires a careful understanding of the characteristics of urban residential lifestyle and consumption. The production-based accounting approach based on top-down statistical data has a limited ability to reflect the total greenhouse gas (GHG) emissions from residential consumption. In this paper, we present a survey-based GHG emissions accounting methodology for urban residential consumption, and apply it in Xiamen City, a rapidly urbanizing coastal city in southeast China. Based on this, the main influencing factors determining residential GHG emissions at the household and community scale are identified, and the typical profiles of low, medium and high GHG emission households and communities are identified. Up to 70% of household GHG emissions are from regional and national activities that support household consumption including the supply of energy and building materials, while 17% are from urban level basic services and supplies such as sewage treatment and solid waste management, and only 13% are direct emissions from household consumption. Housing area and household size are the two main factors determining GHG emissions from residential consumption at the household scale, while average housing area and building height were the main factors at the community scale. Our results show a large disparity in GHG emissions profiles among different households, with high GHG emissions households emitting about five times more than low GHG emissions households. Emissions from high GHG emissions communities are about twice as high as from low GHG emissions communities. Our findings can contribute to better tailored and targeted policies aimed at reducing household GHG emissions, and developing low GHG emissions residential communities in China. © 2013 Lin et al.

Sun Y.,Chinese Institute of Urban Environment | Wang R.,Chinese Institute of Urban Environment | Wang R.,Xiamen Key Laboratory of Urban Metabolism | Liu J.,Chinese Institute of Urban Environment | And 4 more authors.
Applied Energy | Year: 2013

Effective spatial planning is crucial for cost-effectively and sustainably developing biomass energy resources due to the diffuse nature of biomass and high transportation cost. Using the spatial analysis technology, economic models and scenario analysis, this paper presents a spatial planning framework to identify the appropriate developing areas of biomass energy at regional level. The methodology is applied in a case study of Fujian Province, China. Firstly, spatial distribution of two kinds of biomass resources and the technical potential, i.e. the amount of power generation from agricultural and forestry residues in each supply area, were estimated by incorporating the spatial data and the statistical data. The results indicate that total technical potential of agricultural and forestry residues is estimated at 25.13TWhy-1, equivalent to approximately 19% of total electricity consumption in Fujian in 2010. In the second step, the economic analysis assesses the cost of biomass generation for each supply area on the basis of current market conditions. Ranking of the supply areas is then performed by using the priority development index (PDI), which can measure the priority of each biomass supply area by combining several influencing indicators. Finally, the selection of supply areas for power plants can be carried out according to its order in PDI until the total planed capacity in the region is met. The priority of the subregions and the corresponding cost of biomass generation for different planning scenarios can be explicitly visualized. The methodology can be applied to a wide area and can support the local authorities to define and implement a strategy for future biomass energy development. © 2013 Elsevier Ltd.

Wang R.,Chinese Institute of Urban Environment | Wang R.,Xiamen Key Laboratory of Urban Metabolism | Liu W.,Chinese Institute of Urban Environment | Xiao L.,Chinese Institute of Urban Environment | And 3 more authors.
Energy Policy | Year: 2011

Following the announcement of the China's 2020 national target for the reduction of the intensity of carbon dioxide emissions per unit of GDP by 40-45% compared with 2005 levels, Chinese provincial governments prepared to restructure provincial energy policy and plan their contribution to realizing the State reduction target. Focusing on Fujian and Anhui provinces as case studies, this paper reviews two contrasting policies as a means for meeting the national reduction target. That of the coastal province of Fujian proposes to do so largely through the development of nuclear power, whilst the coal-rich province of Anhui proposes to do so through its energy consumption rate rising at a lower rate than that of the rise in GDP. In both cases renewable energy makes up a small proportion of their proposed 2020 energy structures. The conclusion discusses in depth concerns about nuclear power policy, energy efficiency, energy consumption strategy and problems in developing renewable energy. © 2011 Elsevier Ltd.

Lin J.-Y.,Chinese Institute of Urban Environment | Lin J.-Y.,Xiamen Key Laboratory of Urban Metabolism | Lin T.,Chinese Institute of Urban Environment | Lin T.,Xiamen Key Laboratory of Urban Metabolism | And 2 more authors.
Ecological Indicators | Year: 2012

Ecological indicator system (EIS) is widely used in ecosystem monitoring, assessment and management, building a bridge between scientists, environmental managers, and the general public. This paper explores a conceptual model for ecological indicator selection, and a quantitative selection model is formulated based on the conceptual model. The quantitative selection model is a typical zero-one type integer programming problem, and a binary-code genetic algorithm is proposed for solving it. Then the quantitative selection model and its solving method are applied to the Xiamen's coastal ecosystem health framework which comprises 4 levels of ecosystem structure. In this case, the purpose is to reduce the indicator set to minimize overall management costs, and 19 indicators are discarded from the 54 candidate concrete indicators by our method. The selection modeling and its solving method are demonstrated to be a scientific and effective way for ecological indicator application. © 2011 Elsevier Ltd.

Chen F.,Chinese Institute of Urban Environment | Chen F.,Xiamen Key Laboratory of Urban Metabolism | Zhao X.F.,Chinese Institute of Urban Environment | Zhao X.F.,Xiamen Key Laboratory of Urban Metabolism
IOP Conference Series: Earth and Environmental Science | Year: 2014

According to radiation transfer equation (RTE), it is an ill-conditioned problem to obtain land surface temperature (LST) accurately from the HJ-1B thermal infrared channel. Several algorithms have been proposed to resolve this problem. However, some accurate inputs (e.g. atmospheric parameters and land surface emissivity) always inaccessible to common users are indispensable to their applications. An innovative approach (named MTSC method) based on multi-temporal data was described in this paper, by means of which the LSTs are able to be estimated readily and directly from the radiometrically corrected thermal images, even without any other accurate information. To demonstrate its capability, four HJ-1B images (acquired on Nov 28, Dec 10, Dec 18 and Dec 22, 2011, respectively) mainly covering the Pearl River Delta region were selected for LSTs estimation. The LSTs retrieved by the MTSC method were then compared with the near time MODIS surface temperature products, and the samples were collected through a proper procedure. The preliminary assessments demonstrated that accurate results were obtained by using this new method. For example, for the retrieved results of Dec 22, the systematic errors for land cover and sea area were approximate to 1K and 0.5K, respectively. Further comparisons show that the temporal influence was negligible in this experiment, mainly because of the moderate impacts arisen from the atmospheric variation on the surface thermal property, which was acceptable for the MTSC method. However, modifications and improvements are still necessary to enable the full usage of this new approach.

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