A qualitative method is presented for geographic information retrieval (GIR) to support qualitative representation, semantic matching, reasoning and ranking. The novel approach can avoid semantic information lost in current quantitative GIR methods. Information in documents and user queries are represented by propositional logic, which considers the thematic and geographic semantics synthetically. The similarity between documents and queries can be divided into thematic similarity and geographic similarity. The former is calculated by the weighted distance of proposition keywords in domain ontology, and the latter is further divided into conceptual similarity and location similarity which are measured by geo-ontology and spatial semantic respectively. Represented by propositions and information units, the similarity measurement takes evidence theory and fuzzy logic to obtain a general similarity from all sub similarities. This novel method retrieves qualitative geographic information from web and ranks documents semantically, which is consistent with commonsense, and thus can improve the efficiency of geographic information retrieval technology.

The authors present a new approach for modeling qualitative geographic information retrieval based on the qualitative representation, semantic matching and qualitative reasoning methods. Information in web document and user queries are represented and extracted by a new qualitative method considering the thematic and geographic semantics synthetically. The novel retrieval method is used to retrieve the qualitative geographic information to support the semantic matching and results ranking. Finally, a prototype system is implemented to evaluate this research. Through these works, the geographic information retrieval technology is much adaptive to human commonsense cognition and behaviors. The efficiency of geographic information retrieval is improved with the qualitative representation and qualitative spatial reasoning method.

The authors investigate greenhouse gas (GHG) emissions of the Chinese economy by producing sectors from production-based and consumption-based perspectives. A sectoral GHG emissions matrix is built based on Chinese Economic Input-Output Life Cycle Assessment (EIO-LCA) Model 2007 to incorporate the two perspectives in the same analysis framework. This matrix provides a better understanding of the relationship between GHG emissions from production and final demand. The results show that electric and heat power sector contributes the most direct emissions, 36.24% of total emissions, of which 93.91% are due to the production for other sectors, especially construction sector. Final demand for construction sector acounts for the highest embodied emissions, 29.79% of total emissions, of which 97.10% are emitted in other sectors of the supply chain, especially electric and heat power sector. Electric and heat power sector holds the top embodied emission intensity of 9.88 ton CO₂-eq/104 yuan, of which direct emission intensity contributes 89.70%.

Based on analysis of the characteristic of the tourist urban environments, this paper pertinently proposed an inexact fuzzy multi-objective system programming and optimizing method. It is the first time that the inexact fuzzy multi-objective planning method is applied in the field of tourist urban environmental planning. And then, an inexact fuzzy multi-objective system programming and optimizing model for the tourist urban environment is also constituted. This model is not only integrated the kinds of environmental factors and economic factors, but also given full consideration to the uncertainty of the tourist urban environmental development. Through the interactive process between man and computer, the ideas and knowledge of experts and decision-makers are combined with the model. So the scientific nature and practicability of the planning results are guaranteed.

Based on the characteristics of the environmental planning for economic development zone, this paper proposes an Inexact Multiobjective Mixed Integer Programming (IMOMIP) model which can be applied to solve the problems of multiobjectives, dynamic and uncertainty, and an interactive algorithm which based on the Objective-Deviation-Tolerance-Level(ODTL) is developed to solve the model. The IMOMIP and its solution algorithm can reflect the uncerain information through the solution process and in the result, at the same time, the coordination of different objectives which did not possess clear correlation can be achieved.