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The Yangtze Economic Belt (hereafter, the Belt) is one of the most dynamic regions in China in terms of population growth, economic progress, industrialization, and urbanization. It faces many resource constraints (land, food, energy) and environmental challenges (pollution, biodiversity loss) under rapid population growth and economic development. Interactions between human and natural systems are at the heart of the challenges facing the sustainable development of the Belt. By adopting systematic thinking and the methodology of system dynamics simulation, an integrated system-dynamics-based simulation model for the Belt, named ANEMI_Yangtze, has been developed based on the third version of ANEMI3. The nine sectors of population, economy, land, food, energy, water, carbon, nutrients, and fish are currently included in ANEMI_Yangtze. This paper presents the ANEMI_Yangtze model description, which includes (i) the identification of the cross-sectoral interactions and feedbacks involved in shaping the Belt's system behavior over time; (ii) the identification of the feedbacks within each sector that drive the state variables in that sector; and (iii) the description of a new fish sector and modifications to the population, food, energy, and water sectors, including the underlying theoretical basis for model equations. The validation and robustness tests confirm that the ANEMI_Yangtze model can be used to support scenario development, policy assessment, and decision-making. This study aims to improve the understanding of the complex interactions among coupled human–natural systems in the Belt to provide the foundation for science-based policies for the sustainable development of the Belt. [ABSTRACT FROM AUTHOR]

Timely and accurate socioeconomic indicators are the prerequisite for smart social governance. For example, the level of economic development and the structure of population are important statistics for regional or national policy-making. However, the collection of these characteristics usually depends on demographic and social surveys, which are time- and labor-intensive. To address these issues, we propose a machine learning-based approach to estimate and map the economic development from multi-source open available geospatial data, including remote sensing imagery and OpenStreetMap road networks. Specifically, we first extract knowledge-based features from different data sources; then the multi-view graphs are constructed through different perspectives of spatial adjacency and feature similarity; and a multi-view graph neural network (MVGNN) model is built on them and trained in a self-supervised learning manner. Then, the handcrafted features and the learned graph representations are combined to estimate the regional economic development indicators via random forest models. Taking China's county-level gross domestic product (GDP) as an example, extensive experiments have been conducted and the results demonstrate the effectiveness of the proposed method, and the combination of the knowledge-based and learning-based features can significantly outperform baseline methods. Our proposed approach can advance the goal of acquiring timely and accurate socioeconomic variables through widely accessible geospatial data, which has the potential to extend to more social indicators and other geographic regions to support smart governance and policy-making in the future. [ABSTRACT FROM AUTHOR]

Yangtze Economic Belt is one of the most dynamic regions in China in terms of population growth, economic progress, industrialization, and urbanization. It faces many resource constraints (food, energy) and environmental challenges (pollution, biodiversity loss) under rapid population growth and economic development. Interactions between human and natural systems are at the heart of the challenges facing the sustainable development of the Yangtze Economic Belt. Understanding these interactions poses challenges because human and natural systems evolve in response to a wide range of influences. Accounting for these complex dynamics requires a system tool that can represent the fundamental drivers of change and responses of the individual system as well as how different systems interact and co-evolve. By adopting the system thinking and the methodology of system dynamics simulation, an integrated assessment model for the Yangtze Economic Belt, named ANEMI_Yangtze, is developed based on the third version of the global integrated assessment model, ANEMI. Nine sectors of population, economy, land, food, energy, water, carbon, nutrients, and fish are currently included in ANEMI_Yangtze. This paper identifies the opportunities and challenges facing the Yangtze Economic Belt and presents the ANEMI_Yangtze model structure. It also includes: (i) the identification of the cross-sectoral interactions and feedbacks involved in shaping Yangtze Economic Belt's system behaviour over time; (ii) the identification of the feedbacks within each sector that drive the state variables in that sector; and (iii) the explanation of the theoretical and mathematical basis for those feedbacks. ANEMI_Yangtze was developed and calibrated sector by sector before coupling them together into complete ANEMI_Yangtze model. After the validation and robustness test, the ANEMI_Yangtze model can be used to support decision making, policy assessment, and scenario development. This study aims to improve the understanding of the complex interactions among human and natural systems in the Yangtze Economic Belt to provide foundation for science-based policies for the sustainable development of the economic belt. [ABSTRACT FROM AUTHOR]

Karst rocky desertification occurs after vegetation deteriorates as a result of intensive land use, which leads to severe water loss and soil erosion and exposes basement rocks, creating a rocky landscape. Karst rocky desertification is found in humid areas in southwest China, the region most seriously affected by rocky desertification in the world. In order to promote ecological restoration and help peasants out of poverty, the Chinese government carried out the first phase of a rocky desertification control project from 2006 to 2015, which initially contained the expansion of rocky desertification. Currently, the Chinese government is prepared to implement the second phase of the rocky desertification control project, and therefore it is essential to summarise the lessons learned over the last 10 years of the first phase. In this paper, we analyse the driving social and economic factors behind rocky desertification, summarise the scientific research on rocky desertification in the region, and finally identify the main problems facing rocky desertification control. In addition, we put forward several policy suggestions that take into account the perspective of local peasants, scientific research, and China's economic development and urbanisation process. These suggestions include promoting the non-agriculturalization of household livelihoods, improving ecological compensation, strengthening the evaluation of rocky desertification control and dynamic monitoring, and strengthening research on key ecological function recovery technologies and supporting technologies. [ABSTRACT FROM AUTHOR]

Based on the data of China's fourth, fifth and sixth population census, taking the seven geographical zone as research units, the Changing Spatial Patterns of China's Migration in 1985-2010 is studied by the means of spatial analysis and mathematical statistics. The empirical results show that: 1 The migration population in China was increasing a lot in 1985-2010, and the main part of it is Provincial migration. 2 The total number of migration, immigration and emigration, the relative proportion of inter provincial and provincial migration have been positively correlated to the regional economic development level. 3 The emigrations from Hong Kong, Macao, Taiwan and overseas mainly moved to East and North China. 4 Central and west of China are the main area where people outflowed from, and most migration population moved to south-eastern coastal areas. The migration in Northeast and northwest of China is still relatively small. The main direction of population migration and flowing is from west to east and from north to south. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are responsible for devastating impact and mass losses. Consequently, it is essential to diagnose all components of risk in order to quantitatively estimate how the TC-induced direct economic losses (DELs) respond to the changes in hazard, exposure and vulnerability. The probability distribution of TC-induced DEL records over the period 2000−2015 for the mainland of China appears to be a negative binomial. This paper quantifies the relationship between TC-induced DELs (L), maximum sustained wind (MSW), asset value (K) and per capita GDP (I) based on the economic concept of elasticity by the generalized linear model. The coefficients on three terms indicate that a doubling in MSW increases the DEL by 303%, a doubling in asset value exposure increases the DEL by 65%, while a doubling in per capita GDP leads to a 53% decrease in DEL. The results can be used for loss assessment of disasters that occur afterwards. The elasticity values suggest that continuing economic growth will take a heavy toll on TC-prone regions assuming no changes in mitigation and adaptation efforts. From another perspective, the results emphasize that human behavior matters to the formation of disasters and disaster risk reduction. [ABSTRACT FROM AUTHOR]

Sustainable Development Goals (SDGs) proposed by the United Nations (UN) General Assembly in September 2015, provided a global development agenda for all countries to use as a blueprint for progress on economic, social, and environmental sustainability. There are 17 Goals, 169 Targets and 230+ Indicators in SDGs, which specifically depends on multi-source data for monitoring and reporting on progress towards the associated targets and indicators. The existing evaluation methods are mainly based on statistic and survey data with lower spatiotemporal resolution, longer evaluation cycle, and higher cost. To monitor indicators of the SDGs quickly and accurately, it required us to explore new data sources and technology. In this paper, firstly, we reviewed comprehensively all indicators information (concepts and definitions, computation method, and data sources) in three Goals (SDG6, SDG11, and SDG15). Then, we analyzed the feasibility of monitoring accurately and quickly the above indicators on basis of available multi-source data (statistics, remote sensing, ground-based observations and internet data). Finally, a series of integration methods are developed to monitor several specific indicators with multi-source data. The methodology of this study provided experience for monitoring SDGs indicators in developing countries and filled the gap for the assessment of SDGs indicators at fine scale. [ABSTRACT FROM AUTHOR]

The North China Plain (NCP) has become one of the most polluted regions in China, with the rapidly increasing economic growth in the past decades. High concentrations of ambient O3 and aerosol have been observed at urban as well as rural sites in the NCP. Most of the in situ observations of air pollutants have been conducted near the ground so that current knowledge about the vertical distributions of tropospheric O3 and aerosol over the NCP region is still limited. In this study, vertical profiles of O3 and size-resolved aerosol concentrations below 2.5 km were measured in summer 2014 over a rural site in the NCP, using an unmanned aerial vehicle (UAV) equipped with miniature analyzers. In addition, vertical profiles of aerosol scattering property in the lower troposphere and vertical profiles of O3 below 1 km were also observed at the site using a lidar and tethered balloon, respectively. The depths of the mixed layer and residual layer were determined according to the vertical gradients of lidar particle extinction and aerosol number concentration. Average O3 and size-resolved aerosol number concentration in both the mixed and residual layer were obtained from the data observed in seven UAV flights. The results show that during most of the flights the O3 levels above the top of mixed layer were higher than those below. Such a positive gradient in the vertical distribution of O3 makes the residual layer an important source of O3 in the mixed layer, particularly during the morning when the top of mixed layer is rapidly elevated. In contrast to O3, aerosol number concentration was normally higher in the mixed layer than in the residual layer, particularly in the early morning. Aerosol particles were overwhelmingly distributed in the size range<1 µm, showing slight differences between the mixed and residual layers. Our measurements confirm that the lower troposphere over the rural area of the NCP is largely impacted by anthropogenic pollutants locally emitted or transported from urban areas. Compared with the historic O3 vertical profiles over Beijing from the Measurement of Ozone and Water Vapor by Airbus In-Service Aircraft (MOZAIC), a strong increase in O3 can be found at all heights below 2.5 km in the decade from 2004 to 2014, with the largest enhancement of about 41.6 ppb. This indicates that the lower troposphere over the northern part of the NCP has experienced rapidly worsening photochemical pollution. This worsening trend in photochemical pollution deserves more attention in the future. [ABSTRACT FROM AUTHOR]