Urban agglomeration, an established urban spatial pattern, contributes to the spatial association and dependence of city-level CO2 emission distribution while boosting regional economic growth. Exploring this spatial association and dependence is conducive to the implementation of effective and coordinated policies for regional level CO2 reduction. This study calculated CO2 emissions from 2005–2016 in the Chengdu-Chongqing urban agglomeration with the IPAT model, and empirically explored the spatial structure pattern and association effect of CO2 across the area leveraged by the social network analysis. The findings revealed the following: (1) The spatial structure of CO2 emission in the area is a complex network pattern, and in the sample period, the CO2 emission association relations increased steadily and the network stabilization remains strengthened; (2) the centrality of the cities in this area can be categorized into three classes: Chengdu and Chongqing are defined as the first class, the second class covers Deyang, Mianyang, Yibin, and Nanchong, and the third class includes Zigong, Suining, Meishan, and Guangan—the number of cities in this class is on the rise; (3) the network is divided into four subgroups: the area around Chengdu, south Sichuan, northeast Sichuan, and west Chongqing where the spillover effect of CO2 is greatest; and (4) the higher density of the global network of CO2 emission considerably reduces regional emission intensity and narrows the differences among regions. Individual networks with higher centrality are also found to have lower emission intensity.

There are a lot of unused land and land type distribution problems in the land use layout of mountain towns. Mountainous area is widely distributed in China. In the past, scholars focused on the big cities in the planning of land use layout, but paid little attention to the towns in mountainous area.This paper takes Yuexi county, Anqing city, Anhui province as the research area, and based on the principle of multi-objective particle swarm optimization. Firstly, the current land use map was pretreated with unused land and aggregation. The unused land was converted into a high-grade land type according to the suitability map. The aggregation processing increases the aggregation of a certain land type in the eight neighboring areas. Then, the multi-objective function is combined with the particle swarm optimization(PSO) algorithm, and the PSO algorithm is used to update the iterative operation, and the optimal layout scheme is selected. Finally, from the area changes before and after the optimization, economic, ecological benefits and spatial layout of the evaluation. The results showed that the optimized economic and ecological benefits were 9092.7397 million yuan and 40.09377 million yuan higher than the original, respectively. Moreover, after the optimization, the landscape fragmentation index of the original layout was improved by 0.019930486 per hectare compared with that of the original, and the aggregation was greatly improved in space, so the research has corresponding value.

以低碳为目标的土地利用结构优化对区域和全球的碳循环具有重要意义。该文以海东市为例探讨了青海高原东部农业区低碳导向的土地利用结构优化方案。首先计算了海东市主要土地利用类型的碳密度和碳排放系数,然后以最大化碳储量和最小化碳排放量作为目标函数,运用线性规划方法构建了土地利用结构优化模型,获得了土地利用结构优化方案。通过与土地规划方案的对比发现,基于碳储量最大化的优化方案最佳。在该方案下,到2020年海东碳储量达14 171.68万t,碳排放量为217.65万t,比规划方案的碳储量增加了7.77万t,同时减少了31.98万t的碳排放,可见以增加碳储量和减少碳排放为目的的优化方案,对区域固碳作用明显。该结论能为相关部门进行土地利用规划及其调整提供了有效支持。

Optimizing land use structure for low-carbon targets is important to regional and global carbon cycle research. Here,we investigated low-carbon oriented land use structure optimization for Haidong in Qinghai,China,located in the eastern part of the Qinghai Plateau. Carbon storage and carbon emission factors for each land use type in Haidong were calculated based on local carbon mechanisms. Methods for calculating carbon storage and carbon emission estimation have been established systematically. Land use quantitative structure optimization models aimed at maximum carbon storage and minimum carbon emissions were constructed using linear programming methods. Two optimized schemes were obtained using land use quantitative structure optimization models. By comparing land use planning schemes with that of carbon storage maximizing and carbon emission minimizing we found that although the carbon emission minimizing scheme in Haidong is capable of bringing carbon emissions to the lowest level in 2020,the corresponding carbon accumulation is much less than that of the land use planning scheme. The carbon storage maximizing scheme can increase carbon storage in Haidong to 141.71×106t in 2020,which is 77.7×103t higher than the land use planning scheme. However,this maximizing scheme can decrease carbon emissions to 2.17×106t,which is 319.8×103t lower than for land use planning. In comparing the land use programming scheme,the main advantages of the carbon storage maximizing scheme would be that it increases the rate of increasing forestry and dampens the expansion rate of construction land. Therefore,low-carbon oriented land use structure optimization is good support for government land use planning and adjustment.

Traditional cell-based cellular automata (CA) models use a regular cellular grid to represent geographic space, and new approaches to CA models have explored the use of a vector representation of space instead of a regular grid to characterize urban space more realistically. However, less attention has been paid to modeling the interaction between the geospatial information and the irregular cells. To date, the majority of spatial boundaries have been created by individual agencies in an uncoordinated manner. As a consequence, the potential uses of the data collected for land-use change models are limited. In this paper, we propose a new vector-based CA model based on a new constrained irregular space representation using the theory of hierarchical spatial reasoning. For dividing the geographic space considering different items, first land patches are considered as the minimum division unit; then aggregation rules, including attribute, geometric and boundary barrier constraints, are defined; and finally different levels of spatial units are formed through land patches based on aggregation rules. The proposed model is used to simulate the land-use changes in Nanjing, Jiangsu Province, China. The performance validation and comparison illustrate the feasibility of the proposed space representation in a CA model. By using this model, it is expected that the use of the real spatial boundaries that are employed in urban planning could help provide a flexible paradigm to consider various drivers or constraints for realistically simulating land-use changes.

城市增长边界是管控城市建设用地无序扩张的有效手段,科学合理划定城市增长边界是当前研究关注的重要课题。本研究试图引入百度动态交通时间和POI数据改进FLUS模型,以长沙市中心城区为例,采用2000、2010和2018年3期土地利用数据对比验证改进FLUS模型模拟精度,并利用改进FLUS模型设置2种情景,模拟2030年长沙市中心城区土地利用变化,结合用地适宜性评价划定城市增长边界。结果显示：① 纳入动态数据的改进FLUS模型模拟2010年和2018年土地利用相比原模型KAPPA系数提高了2.90%和2.74%,总体精度提高了1.79%和1.83%,表明改进模型具有更高模拟精度;② 利用改进FLUS模型模拟的2030年长沙市中心城区土地利用变化,基准情景和生态保护情景建设用地规模分别为930.06 km²和881.36 km²,均以耕地转为建设用地比例最大;③ 长沙市中心城区刚性增长边界范围为1479.59 km²,占中心城区总面积的37.38%,边界内包含了芙蓉区、天心区、雨花区、岳麓区和开福区的大部分区域;④ 基准情景和生态保护情景下,长沙市中心城区弹性增长边界面积分别为799.35 km²和742.92 km²,建设用地扩张空间主要为长沙县和望城区,结果与2010版长沙市城市总体规划拓展方向一致。纳入动态数据的改进FLUS模型多情景模拟划定城市增长边界,能更高精度的为规划决策提供科学依据。

多模型耦合已经成为国内外模拟LUCC有效途径之一。论文通过如下步骤阐明宏观耕地撂荒格局和微观主体行为间的互动机理。首先,将微观主体间的相互作用纳入其决策,构建有限理性多智能体决策模型（Multi-Agent System,MAS）;其次,通过SNNS平台的训练学习以及与历史数据对比分析,验证土地转换模型（Land Transformation Model,LTM）模拟研究区宏观撂荒格局的有效性;最后,依据多模型耦合机理,耦合MAS模型与LTM模型,形成耕地撂荒模拟模型（Cropland Abandonment Simulation Model,CASM）,并基于研究区耕地撂荒的实际数据,探讨模型的合理性和可行性。结果表明：与2013年历史土地利用数据对比,CASM模型的PCM（Percent Correct Metric）系数为71%,比单独利用LTM模型的模拟精度提高3%,不仅表明CASM能够较好地模拟分析米脂县耕地撂荒空间格局分布,而且可有效揭示宏观耕地撂荒格局的微观驱动机理;同时,文章指出未来研究中要考虑政策和市场的影响,进一步完善不同层次主体决策对撂荒的影响,以此来提高模型对现实耕地撂荒的解释力。

在自然资源整合不断加强,土地利用结构日趋复杂的形势下,采用单一土地利用结构优化方式难以有效满足区域土地利用合理规划的需求。综合考虑生态文明、乡村振兴、城乡融合等土地可持续利用目标,构建土地利用结构优化与布局优化的集成体系,对提升规划适用性具有积极作用。选取常州市金坛区为研究区,以经济效益与生态效益为优化目标,设置自然演变、经济优先和生态优先三种发展情景,在通过MOP（多目标规划）模型进行土地利用结构优化的基础上,利用GeoSOS-FLUS模型实现土地利用布局优化。结果表明：通过集成MOP和GeoSOS-FLUS模型,采用”结构+约束+布局+准则“研究模式探索县域单元下的土地利用结构布局优化,对调整土地利用结构,优化土地利用空间布局有一定帮助。从案例分析结果看,城镇工矿用地数量在自然演变和经济效益优先情景下分别增加了39%和95%;林地在生态效益优先情景下增加了40%;农村居民点在三种情景下分别减少33%、66%和66%。在三种发展情景下,空间格局都将面临城镇空间与生态空间两级分化的现象,后期区域发展中应加强城乡土地利用与生态保护协调发展,促进空间布局均衡化。

Faced with the background of natural resources integration and the more complex land use structure, it is difficult to effectively meet the demand for rational land use planning with a single land use structure optimization method. Considering the objectives of sustainable utilization of land use, ecological civilization, rural development, and urban-rural integration, we constructed an integrated system for land use structure and layout optimization, which will play a positive role in improving the applicability of planning. This study first sets three scenarios of natural evolution, economic priority, and ecological priority, and then uses the MOP model (multi-objective planning model) to calculate the optimal land use structure under different scenarios. Finally, it can carry on land use layout optimization based on the GeoSOS-FLUS model in 2030, Jintan district, Jiangsu province, China. Results show that, by integrating the MOP and GeoSOS-FLUS models and adopting the "Structure + Constraint + Layout + Criterion" approach to optimize the land use structure and layout in the county, it is conducive to optimize the quantitative land use structure and improve the land use spatial distribution; from the results of the case analysis, the area of industrial and mining land in cities and towns has increased by 39% and 95% under the natural evolution and economic efficiency priority scenarios; the forest land has increased by 40% under the eco-efficiency priority scenario; rural settlements decreased by 33%, 66% and 66%, respectively in the three scenarios. Under the three development scenarios, the spatial pattern will face the two-level differentiation of urban space and ecological space. In the later period of regional development, it is necessary to strengthen the coordinated development of urban and rural land use and ecological protection, and promote a balanced spatial distribution.

FLUS模型是一种新型的土地利用变化模拟模型,应用前景广阔。本文通过在FLUS模型的人工神经网络（Artificial Neural Network,ANN）训练模块中引入空间自相关因子来改进模型,以珠江三角洲地区为例,基于2009年、2015年土地利用数据和一系列驱动因子对改进的模型进行了验证,并利用该改进的FLUS模型模拟了2035年研究区在3种情景下土地利用变化格局。结果表明：① 引入空间自相关因子后各地类发生概率分布的预测精度更高,耕地、林地、建设用地、水体和未利用土地的拟合优度ROC值分别从0.819、0.928、0.885、0.855和0.861提高到0.857、0.934、0.890、0.863和0.978;② 改进的FLUS模型的模拟精度有一定的提高,Kappa系数从0.732提高到0.744,FOM系数从0.077升到0.106;③ 情景模拟表明,3种情景下珠江三角洲建设用地和林地均将增加、而耕地均呈减少趋势。但不同情景下模拟的土地利用格局也存在显著差异: 基准情景下,建设用地明显扩张且大幅侵占耕地。耕地保护情景下,耕地面积保持在合理水平,建设用地蔓延扩张趋势得到遏制,土地利用布局总体趋向合理。生态保护情景下,耕地、林地和水体得到较好保护,建设用地布局更为合理,土地利用可持续性明显提高。

Firms must adapt to a business environment in constant flux. Economic and political factors and the constant interruption of new technologies force firms and organizations to change and to adapt, so that they are not left behind. Over recent years, the development of disruptive innovations has completely revolutionized past scenarios. These innovations break with what is already established and firms from various sectors face no choice other than to incorporate them into their project management portfolios, so as to ensure survival and business sustainability. Using MIVES methodology as its foundation, a business sustainability management model is presented in this paper for the management of disruptive innovation projects that a firm may wish to develop within a given sector. The management model is designed to facilitate disruptive innovation project management for firms within technological-industrial sectors, by assessing the sustainability of the project. The model is applied to two firms, one from the machine-tooling sector and another from the construction sector. Finally, a sensitivity analysis was performed, the results of which verified the validity and the stability of the proposed model.

The dynamics of terrestrial carbon are a key factor driving climate change and are greatly influenced by terrestrial processes. Land-use changes as a specific terrestrial process extensively influence terrestrial carbon storage through the alteration of biomass and soil organic matters. However, the direct effect of future land-use changes on terrestrial carbon storage is limited by the refinement of future land-use simulation. In this paper, we propose a FLUS-InVEST (future land use simulation-Integrate valuation of ecosystem services and trade-offs) model to assess the impact of projected land-use change on terrestrial carbon storage in China. The FLUS-InVEST model is composed of fine land-use projection and terrestrial carbon change detecting. Based on the representative showed that: 1) the area with deceased carbon storage shifts from North China to Northeast and the area with increased concentration pathways (RCPs) scenarios, we projected a future land-use change with 30 m resolution for 2100 in China and simulated the changing in terrestrial carbon storage. The FLUS results had a Kappa value of 0.74 and an overall accuracy with 0.80, which indicated that the FLUS performed well in fine land-use projection on a national scale. In addition, carbon storage shifted from Northwest to Southwest between 1995 and 2010. 2) In the RCPs scenario, carbon storage in the forests continues to increase, but decreased in grassland. Specifically, the carbon storage in ?he forest was predicted to increase by 2 332.64 Tg (1 Tg = 1012 g) and 1 754.59 Tg in RCP 6.0 and RCP 8.5, respectively. The carbon storage in grassland was predicted to decrease by 1 719.03 Tg and 2 468.80 Tg in RCP 6.0 and RCP 8.5, respectively. 3) In RCP 6.0, the carbon stored in aboveground vegetation and soil was projected to increase by 127.12 and 83.67 Tg. Conversely, the carbon stored in RCP 8.5 was projected to decrease by 24.67 Tg and 32.41 Tg in aboveground vegetation and soil, respectively, which indicated that RCP 6.0 contributes more to the future carbon sink than RCP 8.5. 4) In the RCPs scenario, the area with increased carbon was mainly located in lines of Hengduan-Qinling-Taihang-Great Khingan and Xufeng-Taihang-Great Khingant, and the area with decreased carbon was distributed throughout the Yunnan-Guizhou Plateau, Sichuan Basin, and Beijing-Tianjing-Hebei Region.

改革开放以来,中国经济在飞速发展的同时,生态环境问题日益严峻。为保障国家和地区的生态安全,对未来生态空间进行模拟预测十分必要。在长江大保护和长江经济带绿色发展背景下,以湖北省为研究区,利用FLUS模型基于湖北省2010年、2015年土地利用数据及包含自然和人文因素的15种驱动因子数据,对2035年的湖北省生态空间进行模拟预测。结果表明：利用2010年土地利用现状模拟出的2015年湖北省土地利用变化情况,总体精度达到0.976,Kappa系数达到0.961,模拟精度较高。设置的生产空间优先、生活空间优先、生态空间优先以及综合空间优化4种不同情景,基本满足未来湖北省不同发展导向的需求。从地貌单元角度来看,在不同情景下,湖北省生态空间主要分布于湖北省边陲四大山区,中部江汉平原生态空间零星分布。从数量规模上来看,不同情景下各个用地类型数量规模差异较为明显,生产空间优先情景下耕地面积增加1216 km²,生活空间优先情景下城镇用地规模增加5959 km²,生态空间优先情景下生态空间用地增长722 km²,综合空间优化情景下生态空间用地规模变化更趋于平缓。从生态空间变化分布来看,四大山区的生态空间变化不大,但中部江汉平原生态空间变化较为明显,其中从行政区划上来看,变化范围主要分布于武汉城市圈、襄阳市、宜昌市中西部地区及随州市中部地区。总而言之,FLUS模型对于湖北省生态空间模拟的适用性较好,多情景模拟结果可为湖北省未来国土空间规划及未来生态空间管控提供多角度、多方向的政策决策参考。

Since reform and opening up in 1978, with the rapid development of China's economy, the problem of ecological environment has become increasingly serious. In order to ensure the ecological security of the countries and regions, it is necessary to simulate and predict the future ecological space. Under the background of the Yangtze River protection strategy and the green development of the Yangtze River Economic Belt, this paper takes Hubei province as the research area. Based on the data of land use in 2010 and 2015 and the data of 15 driving factors including natural and human factors, the FLUS model was used to simulate and predict the ecological space of Hubei province in 2035. Based on the current situation of land use in 2010, the results show that the overall accuracy of land use change in Hubei in 2015 is 0.976, and the Kappa coefficient is 0.961, which are of high accuracy. The four scenarios of production space priority, living space priority, ecological space priority and comprehensive space optimization basically meet the needs of different development orientations in this province in the future. From the perspective of geomorphic unit, under different scenarios, the ecological space of Hubei is mainly distributed in the four major mountainous areas on the border of Hubei province, and the ecological space in the central Jianghan Plain is scattered. In terms of quantity scale, the difference in the size and scale of each land use type in different scenarios is more obvious. The area of cultivated land increased by 1216 km2 under the priority of production space, and the scale of urban land use increased by 5959 km2 under the priority of living space. The ecological space used in the ecological space priority scenario increased by 722 km2, while that used in the integrated space optimization scenario became moderate. From the distribution of ecological space change, the ecological space of the four mountainous areas has not changed much, but the ecological space of the central Jianghan Plain is more obvious. From the perspective of administrative divisions, the scope of change is mainly distributed in Wuhan City Circle, Xiangyang city, the central-west of Yichang city and the central part of Suizhou city. All in all, the FLUS model has good applicability to the ecological spatial simulation in Hubei. The results of multi-scenario simulation can provide a multi-angle and multi-directional policy decision reference for the future territorial spatial planning and ecological spatial control in this province.