Search

Integrated ocean management (IOM) aligns with the United Nations' Sustainable Development Goals (SDGs) and serves as a crucial strategy for promoting the enduring health of marine ecosystems and the sustainable utilization of marine resources. An analysis of the evolution of China's integrated ocean management policy (IOMP) is crucial for providing valuable guidance in achieving sustainable growth in marine management and the marine economy for both China and other coastal nations worldwide. This paper studies the evolution characteristics, challenges, and prospects of IOMP in China since 1978 using content analysis. The findings indicate that the evolution of IOMP can be categorized into four stages: the germination period (1978–1998), the construction period (1999–2009), the systematization period (2010–2017), and the strategization period (2018–present), based on the state of international marine management. The IOMP has transitioned over time from its initial emphasis on resource development during the germination period to a strategization period that prioritizes the full development of all parts of the system. However, the ultimate goal has consistently been to achieve harmonious coexistence between people and the sea. China's IOMP has experienced a progressive development; nevertheless, it continues to encounter obstacles such as the pressing requirement to revise sea-related policies and the absence of policy alignment. The objective of this paper is to explore the evolution, challenges, and prospects of China's IOMP to better improve the land–sea coordination policy, strengthen international judicial cooperation. and provide effective policy reference for other maritime countries. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goals, adopted by all United Nations Member States, emphasize sustainable cities and communities, aiming to strengthen development planning and foster positive links between urban and rural areas by 2030. As the newly leading platform for urbanization development, characteristic towns ('Tese Xiaozhen' in Chinese) contribute to the target of sustainable urbanisation, wherein town policies are especially important for leading the sound development of characteristic towns. However, the effect of these characteristic towns policies remains uncertain. Additionally, most related studies have utilized qualitative methodologies in policy evaluation, which may be inadequate to guide actual practice. Thus, in order to accurately acknowledge the effect of characteristic towns policies, a policy evaluation framework is established in this paper, utilising the multiple streams theory, text mining and the Policy Modelling Consistency Index model (PMC-Index model). Then, taking 225 policy documents promulgated in China from 2015 to 2022 as the research objects, policy evaluation indicators were selected by combining the multiple streams theory to improve the PMC-Index model and a text mining method. Thereafter, an empirical analysis was conducted to evaluate the consistency of 10 characteristic towns policies chosen. The results indicate that the mean value of the PMC indexes of the 10 examined policies is 7.13 in the total of 9 points, which means that the general performance of the characteristic town policies is satisfactory, yet the low scores in terms of effectiveness, content, and guarantee. In the end, some recommendations and suggestions are proposed for clarifying administrative attributes, optimizing the integration of urban and rural resources, and adjusting the policy hierarchy to a bottom-up approach. This study not only provides a policy evaluation framework to comprehensively understand the consistency of global urbanization policies but also offers a beneficial reference for promote urban–rural sustainable development. [ABSTRACT FROM AUTHOR]

Although we are in an era of enormous global commitments to ecological restoration (the UN Decade on ER; the Bonn Challenge), little attention has been paid to the importance of sustained commitment to individual restoration initiatives and few resources have been dedicated to monitoring, especially the long-term and broad-scale evaluations that would allow us to understand how basin-scale restoration can result in complex spatiotemporal patterns. Remote sensing offers a powerful tool for evaluating restoration initiatives focused on water management in arid regions, where changes in vegetation growth can be tracked visually with measures like the generalized difference vegetation index (GDVI). In this paper, we evaluate the Comprehensive Treatment Program of the Shiyang River Basin (CTSRB), a landscape restoration initiative in China's northwest, using a widely available remote sensing tool, showing how it can reveal the causes of fluctuating changes in restoration success. We focus on spatiotemporal variation, studying a time series from 2001 to 2020 using regression, trend, and stability analyses for six different divisions of the study region (the study area as a whole, the irrigated areas, the periphery of the irrigated regions, and the districts of Ba, Quanshan, and Hu) to evaluate the effects of the restoration initiative. The study period was divided into four equal-length phases based on the implementation timeline of the CTSRB, which includes one pre- and post-intervention interval and two stages of the CTSRB. We found that the CTSRB has played a positive role in the restoration of vegetation in the Minqin Basin, especially desert vegetation. However, the positive effects were not obvious in the first CTSRB period, which was characterized by a decline in vegetation growth likely caused by the strategy of "close the pumping-wells, transform the land", which reversed a greening trend caused by the unsustainable irrigation of wasteland prior to the project's initiation. During the second phase of the CTSRB, vegetation in the regions of "transform the land" gradually improved, and the growth of desert vegetation gradually improved and expanded as a result of more water flowing out of agricultural zones. The rate of vegetation recovery slowed down during the final phase of the CTSRB, and some areas even experienced a decline in the GDVI. Overall, our findings show that the CTSRB, by integrating water management and allowing for uninterrupted ecological restoration, drove complex regional changes in the GDVI, including successful restoration of desert vegetation. The spatiotemporal variable we observed underscores the importance of long-term commitment to arid land restoration initiatives and the importance of even longer-term monitoring using tools like remote sensing. [ABSTRACT FROM AUTHOR]

In 2017, China released the statement of High-Quality Development in response to the sustainable development goals proposed by the United Nations. As the spatial carriers of human economic, social, and cultural development, cities should be updated in order to enter a new period of high-quality urban development. In this paper, we use a multi-period DID model, consider the opening of high-speed railways as a quasi-natural experiment, and use the panel data of 285 prefecture-level and above cities in China from 2007 to 2020 to empirically analyze the effect of high-speed railway (HSR) opening on urban development quality and explore its transmission mechanism. The empirical results find that the HSR opening can significantly promote the level of high-quality urban development. The results remain the same after robustness tests such as PSM-DID and placebo tests. The study mechanism suggests that the HSR opening can significantly improve the quality of urban development through a population agglomeration effect, while capital agglomeration has a masking effect between the HSR opening and high-quality urban development. Further results show that there is heterogeneity in the impact of HSR opening on the high-quality development of cities in different regions and sizes, in which the boosting effect of HSR opening on the development quality of cities in eastern and western regions is more significant compared to cities in central regions; the boosting effect of HSR opening on the high-quality development level of medium-sized cities is stronger. [ABSTRACT FROM AUTHOR]

In line with the United Nations Sustainable Development Goal (SDG) 6, the main goal of the Special Issue on "Remote sensing for water resources and environmental management" was to solicit papers from a diverse range of scientists around the world on the use of cutting-edge remote sensing technologies to assess and monitor freshwater quality, quantity, availability, and management to ensure water security. Special consideration was given to scientific manuscripts that covered, but were not limited to, the development of geospatial techniques and remote sensing applications for detecting, quantifying, and monitoring freshwater water resources, identifying potential threats to water resources and agriculture, as well as other themes related to water resources and environmental management at various spatial scales. The Special Issue attracted over thirteen peer-reviewed scientific articles, with the majority of manuscripts originating from China. Most of the studies made use of satellite datasets, ranging from coarse spatial resolution data, such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), to medium spatial resolution data, such as the Landsat series, ERA5, Modern-Era Retrospective Analysis for Research and Application Land version 2 reanalysis product (MERRA2), CLSM and NOAH ET, and MODIS (Moderate Resolution Imaging Spectroradiometer). Google Earth Engine (GEE) data, together with big data processing techniques, such as the remote sensing-based energy balance model (ALEXI/DisALEXI approach) and the STARFM data fusion technique, were used for analyzing geospatial datasets. Overall, this Special Issue demonstrated significant knowledge gaps in various big data image processing techniques and improved computing processes in assessing and monitoring water resources and the environment at various spatial and temporal scales. [ABSTRACT FROM AUTHOR]

The Loess Plateau is an ecologically fragile area in China; furthermore, loess landslides are typical forms of geological disasters, which severely limit the sustainable development of the local societies and the economy. Studying the automatic detection of landslides can facilitate disaster prevention and mitigation in the Loess Plateau, and help realize the climate action goal (SDG 13) of the United Nations Sustainable Development Goals (SDGs). This paper takes typical loess areas in China as the research object, and establishes a historical loess landslide sample database based on Google Earth (GEE) image data, with a total of 1451 loess landslides. The automatic detection of loess landslides is implemented by improving the You Only Look Once X (YOLOX) algorithm. The results show that the average accuracy of landslide detection in this method is 95.43%, and the accuracy rate is 96.32%, which effectively combines the earth's big data to realize the automatic detection of loess landslides. The research results provide technical support for the promotion of disaster prevention and mitigation in China's loess regions, the realization of sustainable development goals, and the improvement of natural disaster prevention–resistance–reduction systems. [ABSTRACT FROM AUTHOR]

As China's railways continue to expand into the Qinghai–Tibet Plateau, the number of deep-buried long tunnels is increasing. Tunnel-damaging geothermal disasters have become a common problem in underground engineering. Predicting the potential geothermal disaster areas along the Yunnan–Tibet railway project is conducive to its planning and construction and the realization of the United Nations Sustainable Development Goals (SDGs)—specifically, the industry, innovation and infrastructure goal (SDG 9). In this paper, the Yunnan–Tibet railway project was the study area. Landsat-8 images and other spatial data were used to investigate causes and distributions of geothermal disasters. A collinearity diagnosis of environmental variables was carried out. Twelve environmental variables, such as land surface temperature, were selected to predict potential geothermal disaster areas using four niche models (MaxEnt, Bioclim, Domain and GARP). The prediction results were divided into four levels and had different characteristics. Among them, the area under receiver operating characteristic curve (AUC) and kappa values of the MaxEnt model were the highest, at 0.84 and 0.63, respectively. Its prediction accuracy was the highest and the algorithm results are more suitable for the prediction of geothermal disasters. The prediction results show that the geothermal disaster potential is greatest in the Markam-Deqen, Zuogong-Zayu and Baxoi-Zayu regions. Through jack-knife analysis, it was found that the land surface temperature, active faults, water system distribution and Moho depth are the key environmental predictors of potential geothermal disaster areas. The research results provide a reference for the design and construction of the Yunnan–Tibet railway project and associated sustainable development. [ABSTRACT FROM AUTHOR]

In 2017, China made an ambitious statement of high-quality development (HQD) with which to realize the goals of sustainability proposed by the United Nations. Our paper sheds new light on how the corporate high-quality development is affected by the responsibility system for environmental protection targets using a sample of energy-intensive firms from 2003 to 2018. We calculate the indexes for corporate high-quality development using entropy weighting for the five dimensions: efficiency, innovation, openness, greenness, and social responsibility. Then, we develop a difference-in-differences model to reveal that the responsibility system for environmental protection targets significantly dampens high-quality development of corporations, as the estimated coefficient is −0.0420 with a t-statistic of −2.9384. In contrast with private firms, the high-quality development of state-owned firms shows no significant correlation with environmental policy constraints. The efficiency of capital allocation by corporations mediates the effects of responsibility for environmental protection targets on high-quality development. Our study suggests several policy implications: first, understand the connotation of a high-quality development system, and formulate diversified regulatory policies. Second, the responsibility system for environmental protection targets in China should be implemented steadily within the firm's abilities. Next, the high-quality development of private firms should generate great attention. Finally, corporate internal governance should be designed to improve high-quality development. [ABSTRACT FROM AUTHOR]

Confronted with China's burgeoning population and finite arable land resources, the enhancement of sustainable arable land efficiency is of paramount importance. This study, grounded in the United Nations Sustainable Development Goals (SDGs), introduces a robust framework for assessing sustainable arable land use. Utilizing the Sustainable Utilization of Arable Land (SUA) indicator system, the DGA–Super-SBM model, the Malmquist–Luenberger production index, and the TO–Fisher–OSM algorithm, we evaluated the efficiency of sustainable utilization of arable land (ESUA) in 52 prefecture-level cities within China's major grain-producing regions of the Yellow and Huaihai Seas. We analyzed the cropland utilization patterns from 2010 to 2020, examining the influence of these patterns on sustainable utilization efficiency. Our findings indicate that between 2010 and 2020, the arable land usage in these regions exhibited minimal transformation, primarily shifting towards construction land and conversely from grassland and water systems. Notably, the ESUA of arable land demonstrated an upward trend, characterized by pronounced spatial clustering, enduring high efficiency in the northern regions, and a significant surge in the southern sectors. The declining ESUA (D-ESUA) trend was general but increased in half of the cities. The change in the center of gravity of ESUA correlated with the north–south movement of the proportion of cultivated land area, the turn-in rate, and the turn-out rate, yet moved in the opposite direction to that of cultivated land density and yield per unit area. Variables such as the replanting index, cropland density, yield per unit area, and cropland turn-in rate significantly affected ESUA. These findings offer a scientific basis and decision-making support for optimizing the utilization pattern of arable land and achieving a rational allocation of arable land resources. [ABSTRACT FROM AUTHOR]

The United Nations' 17 Sustainable Development Goals (SDGs) provide a pivotal framework to guide government policies and mobilize public engagement for sustainability, although further exploration is still needed to analyze the attention given to the SDGs by these stakeholders using multi-source data. To comparatively analyze government and public engagement in the SDGs, based on a case study in China, this study employed a multi-source data and content analysis to assess the perceived importance and performance of the two stakeholders. Then, this study performed an importance–performance analysis (IPA) to assess the SDGs' priority for further improvement. The results highlight the government's emphasis on SDG 7, SDG 2, and SDG 9, contrasting with public attention predominantly on SDG 8, SDG 9, and SDG 4. Regarding the performance of the SDGs in China, the greatest achievements have been exhibited for SDG 4 and SDG 1, while SDG 10, SDG 15, and SDG 17 have shown the least progress. Concerning the individual indicators, 12 out of 98 have attained 100% completion, while 19 out of 98 remain below the 50% threshold. According to the IPA results, China should concentrate on SDG 10 and SDG 15 and improve SDGs 6, 7, 14, 16, and 17 as a relatively low priority. The stakeholder analysis indicated that these SDGs are neglected zones by both the public and government. This research innovatively assessed the priorities of SDGs and could strengthen the cooperation between the government and the public to drive the implementation of the Sustainable Development Goals more effectively. This study could contribute to guiding policy directions, inform strategies for public engagement, and enhance the comprehension of sustainable development in China. The framework could serve as a valuable reference for stakeholder engagement in the SDGs in other countries. [ABSTRACT FROM AUTHOR]

The water footprint (WF) is a widely recognised and comprehensive indicator of both the direct and indirect appropriation of freshwater. It has been utilised for diverse functions, including as a key indicator of the planetary boundaries and United Nations Sustainable Development Goals. Focusing on the nation with the greatest WF, i.e., China, this study reviews journal articles both in English and Chinese published from January 2003 to June 2020. Using CiteSpace and bibliometric analysis of papers, journals, and keywords, we explore state-of-the-art WF accounting, driving forces, and effects. Visible differences in WF accounting keywords and spatial scales between English and Chinese literature are identified. Reported WF values for the same product varied across studies, and there was a lack of information regarding uncertainties. Key driving factors have been largely investigated for agricultural WFs but not for other sectors. The WF impact analyses primarily assess the environmental effects, ignoring the associated social and economic impacts. The development of WF studies has improved our understanding of water issues in China. However, there are still existing knowledge gaps to be filled to find solutions to WF-related issues. [ABSTRACT FROM AUTHOR]

The UN 2030 Agenda sets poverty eradication as the primary goal of sustainable development. An accurate measurement of poverty is a critical input to the quality and efficiency of poverty alleviation in rural areas. However, poverty, as a geographical phenomenon, inevitably has a spatial correlation. Neglecting the spatial correlation between areas in poverty measurements will hamper efforts to improve the accuracy of poverty identification and to design policies in truly poor areas. To capture this spatial correlation, this paper proposes a new poverty measurement model based on a neural network, namely, the spatial vector deep neural network (SVDNN), which combines the spatial vector neural network model (SVNN) and the deep neural network (DNN). The SVNN was applied to measure spatial correlation, while the DNN used the SVNN output vector and explanatory variables dataset to measure the multidimensional poverty index (MPI). To determine the optimal spatial correlation structure of SVDNN, this paper compares the model performance of the spatial distance matrix, spatial adjacent matrix and spatial weighted adjacent matrix, selecting the optimal performing spatial distance matrix as the input data set of SVNN. Then, the SVDNN model was used for the MPI measurement of the Yangtze River Economic Belt, after which the results were compared with three baseline models of DNN, the back propagation neural network (BPNN), and artificial neural network (ANN). Experiments demonstrate that the SVDNN model can obtain spatial correlation from the spatial distance dataset between counties and its poverty identification accuracy is better than other baseline models. The spatio-temporal characteristics of MPI measured by SVDNN were also highly consistent with the distribution of urban aggregations and national-level poverty counties in the Yangtze River Economic Belt. The SVDNN model proposed in this paper could effectively improve the accuracy of poverty identification, thus reducing the misallocation of resources in tracking and targeting poverty in developing countries. [ABSTRACT FROM AUTHOR]

Water, energy, food, and ecology are essential for achieving sustainable development in a region, and in order to achieve the Sustainable Development Goals, their security is also essential at a river basin scale. This study investigated the interrelationships among the water system, food system, energy system, and ecosystem in China's Upper Han River, in alignment with Goals 2, 6, 7, and 15 of the United Nations' Sustainable Development Goals (SDGs). To evaluate the achievement of the SDGs in the Upper Han River, this water–energy–food–ecology system was evaluated by a thorough evaluation index system according to Goals 2, 6, 7, and 15, and the weights of the indices were given using a combination of the CRITIC weighting method and entropy approach. The level of coupling coordination of the system from 2000 to 2021 was quantitatively evaluated by using a coupling coordination degree model. The autoregressive integrated moving average model was built to forecast the process of the indices from 2022 to 2041, and the predicted processes of the system were evaluated by the coupling coordination degree model. The degree of coupling coordination improved from 0.396 to 0.845, and the comprehensive assessment development index increased by 113% from 2000 to 2021, demonstrating that it was a stable development period in general. The fragile support capacity of the water system for the energy system, food system, and ecosystem had a great impact on the overall comprehensive evaluation index. SDG2 (food system), SDG6 (water system), SDG7 (energy system), and SDG15 (ecosystem) all have higher levels of internal conflict. These bi-directional dynamics tended to converge in the sufficiency development mode in the future period as well as the historical period. The analysis of the relationship showed that there were inherent connections and interactions between the four goals, as presented by the high level of coupling that persisted between SDG2, SDG6, SDG7, and SDG15. In the process of promoting the achievement of these goals, the coupling degree also tends to be coordinated from 2022 to 2041. The results offer a view for the river basin's sustainable development and management. [ABSTRACT FROM AUTHOR]

Water scarcity is increasingly being recognized as a global concern. Sustainable Development Goal 6 (SDG-6) was established by the United Nations to address water resource governance within its sustainable development framework. This study employs the entropy weight method and a two-stage cyclical dynamic slacks-based measure (SBM) model to scientifically evaluate water resource utilization and governance across various regions in China. The findings reveal notable disparities in both the production and governance efficiency of water resources. Recognizing governance efficiency is crucial for promoting sustainable water resource utilization and socioeconomic development. The eastern, central, and western regions encounter unique challenges in attaining sustainability. The eastern region exhibits minimal potential for enhancing technical efficiency, necessitating interventions in management strategies and resource allocation. Conversely, the challenges in the central and western regions are more pronounced, demanding immediate implementation of new technologies and equipment. The data analysis in this study yields conclusions that offer targeted improvement recommendations to address disparities across China's eastern, central, and western regions, and this is achieved by considering various developmental stages and regional contexts. These recommendations cover areas such as technical support, financial investment, and policy incentives, with the aim of enhancing the sustainable utilization of water resources in the country. [ABSTRACT FROM AUTHOR]

Accurate assessments of the historical and current status of eco-environmental quality (EEQ) are essential for governments to have a comprehensive understanding of regional ecological conditions, formulate scientific policies, and achieve the United Nations Sustainable Development Goals (SDGs). While various approaches to EEQ monitoring exist, they each have limitations and cannot be used universally. Moreover, previous studies lack detailed examinations of EEQ dynamics and its driving factors at national and local levels. Therefore, this study utilized a remote sensing ecological index (RSEI) to assess the EEQ of China from 2001 to 2021. Additionally, an emerging hot-spot analysis was conducted to study the spatial and temporal dynamics of the EEQ of China. The degree of influence of eight major drivers affecting EEQ was evaluated by a GeoDetector model. The results show that from 2001 to 2021, the mean RSEI values in China showed a fluctuating upward trend; the EEQ varied significantly in different regions of China, with a lower EEQ in the north and west and a higher EEQ in the northeast, east, and south in general. The spatio-temporal patterns of hot/cold spots in China were dominated by intensifying hot spots, persistent cold spots, and diminishing cold spots, with an area coverage of over 90%. The hot spots were concentrated to the east of the Hu Huanyong Line, while the cold spots were concentrated to its west. The oscillating hot/cold spots were located in the ecologically fragile agro-pastoral zone, next to the upper part of the Hu Huanyong Line. Natural forces have become the main driving force for changes in China's EEQ, and precipitation and soil sand content were key variables affecting the EEQ. The interaction between these factors had a greater impact on the EEQ than individual factors. [ABSTRACT FROM AUTHOR]

Since the early 1950s, the development of human settlements and over-exploitation of agriculture in the China side of the Amur River Basin (CARB) have had a major impact on the water environment of the surrounding lakes, resulting in a decrease of aquatic vegetation. According to the United Nations Sustainable Development Goals, a comprehensive understanding of the extent and variability of aquatic vegetation is crucial for preserving the structure and functionality of stable aquatic ecosystems. Currently, there is a deficiency in the CARB long-sequence dataset of aquatic vegetation distribution in China. This shortage hampers effective support for actual management. Therefore, the development of a fast, robust, and automatic method for accurate extraction of aquatic vegetation becomes crucial for large-scale applications. Our objective is to gather information on the spatial and temporal distribution as well as changes in aquatic vegetation within the CARB. Utilizing a hybrid approach that combines the maximum spectral index composite and Otsu algorithm, along with the integration of convolutional neural networks (CNN) and random forest, we applied this methodology to obtain an annual dataset of aquatic vegetation spanning from 1985 to 2020 using Landsat series imagery. The accuracy of this method was validated through both field investigations and Google Images. Upon assessing the confusion matrix spanning from 1985 to 2020, the producer accuracy for aquatic vegetation classification consistently exceeded 87%. Further quantitative analysis unveiled a discernible decreasing trend in both the water and vegetation areas of lakes larger than 20 km2 within the CARB over the past 36 years. Specifically, the total water area decreased from 3575 km2 to 3412 km2, while the vegetation area decreased from 745 km2 to 687 km2. These changes may be attributed to a combination of climate change and human activities. These quantitative data hold significant practical implications for establishing a scientific restoration path for lake aquatic vegetation. They are particularly valuable for constructing the historical background and reference indices of aquatic vegetation. [ABSTRACT FROM AUTHOR]

With the growth of global population and economic development, people are facing the problem of increasing scarcity of renewable energy and unsustainable energy use. To achieve the sustainable development goals (SDGs) proposed by the United Nations, research on energy consumption efficiency has become particularly important. This research evaluates the energy consumption efficiency of 270 cities in China through an improved EBM model and finds a common phenomenon of low energy consumption efficiency in the cities, with the highest efficiency in northeast China and the lowest efficiency in eastern China. In addition, the efficiency of industrial exhaust emissions most significantly positively correlates with the efficiency of employed population and total energy consumption efficiency, while the efficiency of regional GDP does not significantly correlate with the efficiency of the two input variables. Using the LMDI method to decompose the driving factors of energy consumption efficiency in the cities, we find that the most important factor affecting energy consumption efficiency is their own energy endowment. Therefore, to improve the energy consumption efficiency of its cities, the China government should comprehensively consider factors such as regional economic development level, industrial structure, and technological level differences, formulate relevant energy-saving and emission-reduction policies, focus on optimizing the energy consumption structure, encourage technological progress and innovation, and help increase investment in science and technology. [ABSTRACT FROM AUTHOR]

The mental health crisis poses a major challenge to global sustainable development. In response, the United Nations has launched an ambitious plan to "reshape the environment that affects mental health". Clean toilets are an important measure of civilization, yet it is unclear whether they are helping to alleviate the global mental health crisis. Therefore, using data from a large sample of rural Chinese households and introducing an instrumental variables approach based on the IV-Tobit model to address endogeneity, this study quantitatively discusses that clean latrines have a positive effect on the mental health of farm households. The results showed the following: (1) 89.8% of Chinese rural households had clean toilets, while the average farm household depression index was 12.568; and (2) clean toilets helped to improve the mental health of farmers, which means that the depression index of farmers with clean toilets decreased by 66.9% compared to farmers without clean toilets. The findings of this study can inform the development of policies to mitigate the global mental health crisis and contribute to the resilience of global development. [ABSTRACT FROM AUTHOR]

The United Nations Sustainable Development Goals (SDGs) and the rise of global sustainability science have led to the increasing recognition of basins as the key natural geographical units for human–land system coupling and spatial coordinated development. The effective measurement of spatiotemporal patterns of urban connectivity within a basin has become a key issue in achieving basin-related SDGs. Meanwhile, China has been actively working toward co-ordinated regional development through in-depth implementation of the Yellow River Basin's ecological protection and high-quality development. Urban connectivity has been trending in urban planning, and significant progress has been made on different scales according to the flow space theory. Nevertheless, few studies have been conducted on the multiscale spatiotemporal patterns of urban agglomeration connectivity. In this study, the urban network in the Yellow River Basin was constructed using Tencent population migration data from 2015 and 2019. It was then divided into seven distinct communities to enable analysis at both the basin and community scales. Centrality, symmetry, and polycentricity indices were employed, and the multiscale spatiotemporal patterns of urban agglomerations in the Yellow River Basin were identified using community detection, complex networks, and the migration kaleidoscope method. Community connectivity was notably concentrated at the basin scale with a centripetal pattern and spatial heterogeneity. Additionally, there was a symmetrical and co-ordinated relationship in population migration between the eastern and western regions of the basin, as well as between the internal and external parts of the basin. At the community scale, there was significant variation in the extent of central agglomeration among different communities, with few instances of similar-level, long-distance, and interregional bilateral links. The utilization of multiscale spatiotemporal patterns has the potential to enhance the comprehension of economic cooperation between various cities and urban agglomerations. This understanding can aid decision-makers in formulating sustainable development policies that foster the spatial integration of the basin. [ABSTRACT FROM AUTHOR]

Achieving food security and improving nutrition is one of the United Nations Sustainable Development Goals. With rapid socioeconomic development, the dietary structure of the Chinese population has changed significantly, leading to increased demand for cultivated land. At the same time, rapid urbanization has continuously reduced the amount of cultivated land in China, and there is an urgent necessity for the nation to alleviate the shortage of cultivated land to meet the population's evolving dietary consumption needs. A review of the literature indicates that the use of mountainous areas to produce agricultural products for the population can effectively reduce the use of cultivated land on the plains and mitigate the shortage of cultivated land to meet dietary consumption needs. According to the different natural and socioeconomic conditions of mountainous areas, this study concludes that the adoption of mountain hillside, mountain understory, and mountainous limited cultivated land use patterns to develop agricultural production in mountainous areas is an effective approach to address the shortage of cultivated land caused by changes in the Chinese dietary structure. [ABSTRACT FROM AUTHOR]

The multispectral imager (MII), onboard the Sustainable Development Science Satellite 1 (SDGSAT-1), performs detailed terrestrial change detection and coastal monitoring. SDGSAT-1 was launched at 2:19 UTC on 5 November 2021, as the world's first Earth science satellite to serve the United Nations 2030 Sustainable Development Agenda. A vicarious radiometric calibration experiment was conducted at the Dunhuang calibration site (Gobi Desert, China) on 14 December 2021. In-situ measurements of ground reflectance, aerosol optical depth (AOD), total columnar water vapor, radiosonde data, and diffuse-to-global irradiance (DG) ratio were performed to predict the top-of-atmosphere radiance by the reflectance-, irradiance-, and improved irradiance-based methods using the moderate resolution atmospheric transmission model. The MII calibration coefficients were calculated by dividing the top-of-atmosphere radiance by the average digital number value of the image. The radiometric calibration coefficients calculated by the three calibration methods were reliable (average relative differences: 2.20% (reflectance-based vs. irradiance-based method) and 1.43% (reflectance-based vs. improved irradiance-based method)). The total calibration uncertainties of the reflectance-, irradiance-, and improved irradiance-based methods were 2.77–5.23%, 3.62–5.79%, and 3.50–5.23%, respectively. The extra DG ratio measurements in the latter two methods did not improve the calibration accuracy for AODs ≤ 0.1. The calibrated MII images were verified using Landsat-8 Operational Land Imager (OLI) and Sentinel-2A MultiSpectral Instrument (MSI) images. The retrieved ground reflectances of the MII over different surface types were cross-compared with those of OLI and MSI using the FAST Line-of-sight Atmospheric Analysis of Hypercubes software. The MII retrievals differed by <0.0075 (7.13%) from OLI retrievals and <0.0084 (7.47%) from MSI retrievals for calibration coefficients from the reflectance-based method; <0.0089 (7.57%) from OLI retrievals and <0.0111 (8.65%) from MSI retrievals for the irradiance-based method; and <0.0082 (7.33%) from OLI retrievals and <0.0101 (8.59%) from MSI retrievals for the improved irradiance-based method. Thus, our findings support the application of SDGSAT-1 data. [ABSTRACT FROM AUTHOR]

Quantitative assessments and dynamic monitoring of indicators based on fine-scale population data are necessary to support the implementation of the United Nations (UN) 2030 Agenda and to comprehensively achieve its 17 Sustainable Development Goals (SDGs). However, most population data are collected by administrative units, and it is difficult to reflect true distribution and uniformity in space. To solve this problem, based on fine building information, a geospatial disaggregation method of population data for supporting SDG assessments is presented in this paper. First, Deqing County in China, which was divided into residential areas and nonresidential areas according to the idea of dasymetric mapping, was selected as the study area. Then, the town administrative areas were taken as control units, building area and number of floors were used as weighting factors to establish the disaggregation model, and population data with a resolution of 30 m in Deqing County in 2016 were obtained. After analyzing the statistical population of 160 villages and the disaggregation results, we found that the global average accuracy was 87.08%. Finally, by using the disaggregation population data, indicators 3.8.1, 4.a.1, and 9.1.1 were selected to conduct an accessibility analysis and a buffer analysis in a quantitative assessment of the SDGs. The results showed that the SDG measurement and assessment results based on the disaggregated population data were more accurate and effective than the results obtained using the traditional method. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goals (SDGs) adopted by the United Nations in 2015 guide the important direction of high-quality urbanization in China's main grain-producing areas (MGPAs), and improving the quality of urbanization is also crucial to achieve the SDGs. China's MGPAs not only undertake the task of promoting urbanization but also of ensuring food security. The establishment of an index system based on SDGs can more effectively measure the urbanization quality of MGPAs. For the specific targets of the SDGs, this study established two sets of multidimensional indicator systems, whether including the goals of food and agriculture, and tracked the progress toward improving urbanization quality of China's MGPAs, including 128 prefecture-level cities, during 2010–2018. We found that the comprehensive urbanization quality and the index of economic efficiency, urbanization level, and environmental quality showed an upward trend with significant regional differences and spatial agglomeration distributions, but the level of agricultural development and urban–rural co-ordination have declined in recent years; the ranking and distribution of urbanization quality, including agricultural development, varied significantly, and the number of cities belonging to the good co-ordination mode decreased as some cities changed to a lower level; and urbanization that does not sacrifice the agricultural capabilities of MGPAs could improve urbanization quality and implement the SDGs. [ABSTRACT FROM AUTHOR]

Groundwater protection is essential for global sustainable development. Due to the lack of motivation among farmers to build harmless disposal facilities for livestock excrement, there is a huge challenge surrounding groundwater protection, which also threatens the achievement of the UN Sustainable Development Goal (SDG) 6: "clean water and sanitation". Aiming to improve the groundwater protection behavior (GPB) of farmers, this study involved the following approach: (1) the use of rural China as a case area; (2) an exploration of the theoretical mechanisms and quantitative impacts of cooperatives as a way to encourage farmers to build harmless disposal facilities for livestock excrement; (3) a discussion about improvement strategies to increase the possibility of farmers building harmless disposal facilities for livestock excrement. The study highlighted the following findings: (1) compared to farmers who did not participate in cooperatives, farmers who were in cooperatives were 1.18% more likely to build harmless disposal facilities for livestock excrement; (2) compared to the basic scenario, the probability of farmers building harmless disposal facilities for livestock excrement could be increased by 50~1300%. The results of this study could help to provide a reference for the introduction of policies to protect groundwater, as well as an experiential reference for the achievement of the UN SDGs 3 and 6. [ABSTRACT FROM AUTHOR]

Studies have shown that household consumption accounts for more than 60% of global greenhouse gas (GHG) emissions. Reducing household CO2 emissions (HCEs) can help combat climate change globally and can provide a wide range of environmental, financial and public health benefits. Here, we present data from a large survey on 14,928 households in eighty-eight Chinese cities to investigate the spatial patterns in HCEs per person (PHCEs) and the drivers behind these patterns based on a multi-scale geographically weighted regression (MGWR) model. We found that higher PHCEs were mainly in northern cities with a severe and cold climate. Our findings suggest that PHCEs could be modeled as a function of household size, education level, income level, consumption tendency and HCEs intensity. HCEs intensity was identified as the most important determinant, and its effect increased from eastern cities to central and western cities in China. The quantification of city-level PHCEs and their drivers help policy makers to make fair and equitable GHG mitigation polices, and they help achieve many of the United Nations Sustainable Development Goals, including affordable and clean energy, sustainable cities and communities, and climate action. [ABSTRACT FROM AUTHOR]

Fine knowledge of the spatiotemporal distribution of the population is fundamental in a wide range of fields, including resource management, disaster response, public health, and urban planning. The United Nations' Sustainable Development Goals also require the accurate and timely assessment of where people live to formulate, implement, and monitor sustainable development policies. However, due to the lack of appropriate auxiliary datasets and effective methodological frameworks, there are rarely continuous multi-temporal gridded population data over a long historical period to aid in our understanding of the spatiotemporal evolution of the population. In this study, we developed a framework integrating a ResNet-N deep learning architecture, considering neighborhood effects with a vast number of Landsat-5 images from Google Earth Engine for population mapping, to overcome both the data and methodology obstacles associated with rapid multi-temporal population mapping over a long historical period at a large scale. Using this proposed framework in China, we mapped fine-scale multi-temporal gridded population data (1 km × 1 km) of China for the 1985–2010 period with a 5-year interval. The produced multi-temporal population data were validated with available census data and achieved comparable performance. By analyzing the multi-temporal population grids, we revealed the spatiotemporal evolution of population distribution from 1985 to 2010 in China with the characteristic of concentration of the population in big cities and the contraction of small- and medium-sized cities. The framework proposed in this study demonstrates the feasibility of mapping multi-temporal gridded population distribution at a large scale over a long period in a timely and low-cost manner, which is particularly useful in low-income and data-poor areas. [ABSTRACT FROM AUTHOR]