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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]

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]

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]