Seeking sustainable development policies at the municipal level based on the triad of city, economy and environment: evidence from Hunan province, China.

Keeping urbanization, economy and eco-environment in harmony is a core issue for attaining Sustainable Development Goals (SDGs) in any complex geographical regions. Previous studies mainly focused on seeking the balance between urban expansion levels, eco-environment quality and socioeconomic degree. But the challenges still exist in solving the negative influence of urban expansion that affects eco-environmental and economic development. Based on the Environmental Kuznets Curve theory, we involved inclusive indexes to analyze the interlinkages of eco-environment quality, economic level, and urban expansion degree, which closely relate to urban sustainable development goals and spatial complexity, as well as using available data corresponding to waterfront cities. Cities in Hunan were taken as a study-case, and the study period of 2006-2016 covers the last 10 years of the millennium development goals agenda and the first 2 years of SDGs agenda. The key indicators of city-economy-environment relationships were different at the provincial level, urban level and urbanization grade. According to the regression models and inverted N shape curve, urban expansion resulted in high positive effects on economic development level and negative effects on ecological environment quality, partically higher at high urbanization level than that of the low ones. But the overall trends were that the environmental quality of the cities was undergoing slowly improving processes both at low and high urban expansion levels. Promoting adaptations with the eco-environmental capacity when formulating policies and taking actions is necessary for realizing sustainable cities and communities (SDG11), life on land (SDG15), decent work and economic growth (SDG8) and responsible consumption and production (SDG12) at the same time. Regulating citizens' density, urban expansion speed in area, the quantity of enterprises with heavy pollution, and the structure of industry to the suitable urbanization stages is an important way for achieving SDGs at provincial and municipal levels.

[Application of simulated annealing method and neural network on optimizing soil sampling schemes based on road distribution].

Taking the soil organic matter in eastern Zhongxiang County, Hubei Province, as a research object, thirteen sample sets from different regions were arranged surrounding the road network, the spatial configuration of which was optimized by the simulated annealing approach. The topographic factors of these thirteen sample sets, including slope, plane curvature, profile curvature, topographic wetness index, stream power index and sediment transport index, were extracted by the terrain analysis. Based on the results of optimization, a multiple linear regression model with topographic factors as independent variables was built. At the same time, a multilayer perception model on the basis of neural network approach was implemented. The comparison between these two models was carried out then. The results revealed that the proposed approach was practicable in optimizing soil sampling scheme. The optimal configuration was capable of gaining soil-landscape knowledge exactly, and the accuracy of optimal configuration was better than that of original samples. This study designed a sampling configuration to study the soil attribute distribution by referring to the spatial layout of road network, historical samples, and digital elevation data, which provided an effective means as well as a theoretical basis for determining the sampling configuration and displaying spatial distribution of soil organic matter with low cost and high efficiency.