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In light of the growing urgency of climate change, carbon emissions reduction has emerged as a pivotal concern within global governance. In this paper, we take carbon emission efficiency (CEE) as the research object to characterize the relationship between economic, social, and environmental development in the context of the Sustainable Development Goals (SDGs). According to the regional division standard of eight comprehensive economic zones in China, this paper analyzed the spatial differences, evolutionary characteristics, and influencing factors of CEE in 257 Chinese cities over the period 2003–2019. The analysis conducted the Dagum Gini Coefficient, Markov Transition Probability Matrix, and geographically and temporally weighted regression model (GTWR). The results demonstrate that: (1) The CEE of Chinese cities exhibits an upward trajectory. (2) The inter-differences among the eight comprehensive economic zones represent the primary spatial source of CEE divergence. (3) The CEE of Chinese cities is a staged process of gradual enhancement with spatial spillover effects. (4) Environmental regulation, energy consumption intensity, and green finances are significant factors affecting CEE, and the direction and intensity of their influence have distinct spatial heterogeneity. Ultimately, this paper proposes measures to narrow the development gap between regions and enhance the CEE across the region. Meanwhile, implementing regional refinement management and formulating differentiated regional sustainable development planning.

Rural resilience is not only a comprehensive reflection of “thriving businesses, pleasant living environments, social etiquette and civility, effective governance, and prosperity”. It is also the unity of resilience in industry, ecology, culture, organization and livelihood. This paper uses the entropy weight-TOPSIS method to measure the rural resilience level in 31 regions in China and analyzes the configuration of influencing factors with the Fuzzy-set qualitative comparative analysis (fsQCA). The results of the study are as follows: (1) The level of rural resilience in China showed a stable increase from 2010 to 2019, but the overall level was low, with large regional disparities, showing a significant positive spatial correlation. (2) In the high-level rural resilience explanatory path, labor-driven, cultural-driven and market–labor–technology linkage-driven play a core role, while administrative force is not playing a significant role. In the explanation path of non-high level rural resilience, the market–labor absent, administrative–market absent and cultural absent hinder the improvement of rural resilience. In summary, we put forward the following suggestions. Policy renovation and support should be strengthened. Adaption to local conditions should be considered in order to achieve sustainable and differentiated development. Development should be coordinated and balanced in different regions so as to achieve an overall resilience level in rural areas.

Under the background of realizing the strategy of advanced country and superior development, the innovative cities pilot, as an important institutional exploration path, can effectively stimulate the innovation vitality of cities, enhance the industrial framework, improve the urban environment, and significantly affect the urban land elements. This research integrates the notion of high-quality development into urban land components, creates an index system for evaluation using the input–output model, and uses the Super-SBM model to evaluate the land-use efficiency of 279 cities nationwide. This paper using a multi-period DID model discusses the impact of innovative city pilot policies on land use efficiency. These are the findings of the study: (1) In terms of average land use efficiency, cities in the eastern region are 0.1333 higher than those in the central region in 2021; The western region is 0.0593 higher than the central region. From 2006 to 2021, the land use efficiency range (the gap between the maximum and the minimum) showed a decreasing trend. (2) The efficient use of urban land can be realized through the execution of the innovative cities pilot. Specifically, contrasted with non-innovative cities, innovative city pilots can increase the importance of efficient urban land usage by roughly 3.6%. And the conclusion passes several tests for robustness. (3) The findings of the mechanism analysis indicate that the innovative cities pilot primarily achieves effective utilization of urban land resources through the path of industrial structure optimization effect, policy guidance effect, and innovation talent gathering effect. (4) Extended analysis reveals that the impact of innovative cities on land use efficiency mainly affects eastern cities and large cities. Based on the research results, this paper proposes to optimize the industrial structure; Strengthen the government’s pollution control; Build a “Smart Land” management system; Countermeasures and suggestions such as improving land use efficiency in accordance with local conditions.

Liaodong Bay is an important aquaculture base in the northernmost part of China’s sea area. With the rapid development of aquaculture and the rapid expansion of aquaculture ponds, the ecological environment of many aquaculture areas has been deteriorating, which has brought about a series of ecological and environmental problems, so it is necessary to carry out rapid, accurate, and sustained monitoring of the near‐coastal aquaculture areas. In this study, we utilized the Google Earth Engine (GEE) platform to extract the spatial distribution and temporal distribution information of aquaculture ponds in Liaodong Bay in a long‐term time series from 1985 to 2020 using Landsat image data, which was validated by accuracy assessment, and then analyzed the distribution, area change, and spatial and temporal evolution of aquaculture ponds. The results showed that the spatial distribution of aquaculture ponds in Liaodong Bay was uneven, with hotspots mainly located in Linghai, Jinzhou; Gaizhou, Yingkou; Suizhong, Huludao; Changxingdao, Dalian; and part of Panjin. The process of change in the area of aquaculture ponds in Liaodong Bay is divided into three phases: the fluctuation period of 1985–1995, the rapid expansion period of 1995–2010, and the stabilization period of 2010–2020, and the aquaculture ponds in Liaodong Bay show a shift from continuous expansion to sustainable development, from quantitative growth to quality and efficiency, and from single‐emphasis on production to energy‐saving and environmentally friendly modes. The results of the study are expected to reveal the current situation, dynamics and development pressure of aquaculture ponds in the coastal area of Liaodong Bay, and to provide information support and decision support for scientific planning of aquaculture in Liaodong Bay.

The high-quality development of the integration of agricultural tourism (IAT) is essential for realizing the rural revitalization strategy. This study investigates the comprehensive development level of harmonious rural construction (HRC) and IAT, as well as their coupling coordination degree, using the entropy weight method, coefficient of variation method, coupling coordination degree model, and panel Tobit model. The study shows that, firstly, the level of HRC in all provinces of China has been steadily increasing, with high-value areas of HRC transitioning from a scattered distribution to a more structured layout between 2011 and 2020. Secondly, IAT has increased across all provinces, following a distribution pattern of \"high in the south and low in the north, high in the west and low in the east\" during the same period. Thirdly, the overall level of HRC and the coupling coordination of agricultural and tourism integration in China remains low, with noticeable regional disparities, as only Shandong Province has reached the primary coordination level. Lastly, urbanization, R&D intensity, economic development, and transportation infrastructure all significantly contribute to the coupled and coordinated development of the two systems. Additionally, factors such as the number of 5A scenic locations, human capital, urbanization level, and openness to the outside world have varying effects on their coordinated development.

Urban agglomeration ecosystems are impacted by human activities and natural disasters, so analyzing the spatial and temporal evolution of landscape ecological resilience from the perspective of adaptive cycling is crucial. Using the Changsha–Zhuzhou–Xiangtan urban agglomeration in China as a case study, this research constructs a “Risk-Potential-Connectivity” framework to evaluate ecological resilience. This framework applies exploratory spatial data analysis methods to examine the spatiotemporal evolution and associated patterns of resilience and the Geodetector model to measure the driving factors of spatial variation. This study constructs an adaptive cycle model based on ecological resilience analysis, integrating potential and connectivity indices to classify the development stages of urban agglomeration regions dynamically. The results showed that the overall spatial distribution pattern of ecological risk decreased from the center outward, whereas ecological potential and connectivity increased. The average resilience index from 2000 to 2020 was 0.31, with a declining trend and shifting center of gravity from northwest to southeast. The spatial and temporal distribution of toughness exhibited high and low aggregation, with an overall Moran index greater than 0.75. Land-use intensity had the strongest explanatory power (q = 0.3662) for the spatial differentiation of landscape ecological resilience drivers and the joint effects of factor interaction had a higher explanatory power than single factors. Adaptive cycle analysis revealed that Furong District is in the protection stage, Xiangtan County in the development stage, and Liling City in the reorganization stage, with no region yet in the release stage. The findings offer a better understanding of the interactive adaptation characteristics and evolutionary patterns of social-ecological systems over extended periods, providing scientific support for the formulation of protection strategies to respond to dynamic changes in urban agglomeration ecosystems.

The validation of remotely sensed evapotranspiration (ET) products is important for the development of ET estimation models and the accuracy of the scientific application of the products. In this study, different ET products such as HiTLL, MOD16A2, ETMonitor, and SoGAE were compared using multi-source remote sensing data and ground-based data to evaluate their applicability in the Heihe River Basin (HRB) during 2010–2019. The results of the comparison with the site observations show that ETMonitor provides a more stable and reliable estimation of ET than the other three products. The ET exhibited significant variations over the decade, characterized by a general increase in rates across the HRB. These changes were markedly influenced by variations in land use and topographical features. Specifically, the analysis showed that farmland and forested areas had higher ET rates due to greater vegetation cover and moisture availability, while grasslands and water bodies demonstrated lower ET rates, reflecting their respective land cover characteristics. This study further explored the influence of various factors on ET, including land use changes, NDVI, temperature, and precipitation. It was found that changes in land use, such as increases in agricultural areas or reforestation efforts, directly influenced ET rates. Moreover, meteorological conditions such as temperature and precipitation patterns also played crucial roles, with warmer temperatures and higher precipitation correlating with increased ET. This study highlights the significant impact of land use and climatic factors on spatiotemporal variations in ET within the HRB, underscoring its importance for optimizing water resource management and land use planning in arid regions.

The objective of this article is to describe the effect of precipitation and temperature on the drought characteristics of Yunnan province in China. The rainfall and temperature data from 10 national meteorological stations in Yunnan province during 1969 to 2018 were used to investigate the spatial and temporal evolution of drought in Yunnan province and the difference of drought index based on Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) at nearly 50a different time scales. The results showed that Yunnan province had a frequent alternation of drought and flood, and the larger the time scale, the gentler the SPI and SPEI changes. The recent 50a drought mainly occurred in 1980–1982, 1988–1990, 2003–2007, and 2010–2015, and there was continuous drought and the duration was prolonged. The changes of SPI and SPEI showed a downward trend in different seasons. The linear slopes of SPI and SPEI in spring, summer, autumn, and winter were − 0.0064, − 0.0088, − 0.0057 and − 0.0.0111, respectively, and the drought trend was the most serious in winter. Continuous spring drought occurred in 2009–2010 and 2012–2014. The SPEI values in 2009–2010 and 2012–2014 were − 0.80, − 0.64, − 0.75, − 1.23, and − 1.17, respectively. The spatial distribution of drought frequency in Yunnan province was greatly different, and its distribution rule was more in the north and east, less in the south and west. The drought frequency in Zhaotong (northeast Yunnan) was the highest at 36.53%, the drought frequency in Deqin and Lijiang (northwest Yunnan) were 33.11% and 33.28%, and the drought frequency in Kunming (central Yunnan) Lincang, Lancang, and Simao (southwest Yunnan) were 29.35%, 30.73%, 32.77%, and 28.35%, respectively. This study provided a scientific basis for revealing the spatial and temporal variation rules, evolution trends, regional drought, and drought impact assessment and risk management of drought in Yunnan province.

This paper firstly analyzes the cognitive mapping driven by knowledge mapping and constructs the framework of the cognitive mapping model of regional landscape with information technology. Secondly, we analyze the spatiotemporal evolution characteristics of the urbanized regional landscape system in terms of the landscape representation layer and the inferential computation layer of the model, including the patch-type dynamic attitude and spatial changes. Finally, based on the urbanization process of city B as an example, the results of the spatial and temporal evolution of its regional landscape types are analyzed. The results show that the proportion of arable land area in the regional landscape type decreased by 13.84% from 2000 to 2020 compared with other landscape types, and the patch density and landscape shape index increased by 2.08 and 22.3 respectively, in 2020 compared with 2000.

The Yellow River Delta in China is the most active one for sea–land changes over all deltas worldwide, and its coastline evolution is critical to urban planning and environmental sustainability in coastal areas. Existing studies rarely used yearly temporal resolution, and lack more detailed and quantitative analysis of coastline evolution characteristics. This paper used visual interpretation to extract the coastline of the Yellow River Delta in year interval Landsat images for 45 years from 1976 to 2020, and analyzed the spatiotemporal characteristics of the coastline evolution through statistical methods such as calculating change values and change rate. The main results are as follows: (1) overall, the coastline of the Yellow River Delta presented a spatial pattern involving northern landward retreat and southern seaward expansion. Since 1990, the Yellow River Delta has entered a period of decline. In addition, the length of the artificial coastline increased by about 55 km; (2) in the Qingshuigou region, the land area and the coastline length increased first and then stabilized. The southeastern part of the Qingshuigou was in a state of erosion, while the northeastern part was expanding toward the sea along the north direction; (3) in the Diaokou region, the land area has been decreasing, but the reduction rate has gradually slowed down. The main conclusions are as follows: (1) through the research on the evolution model and mechanism of the coastline of the Yellow River Delta, it was found that human factors and natural factors were the two major driving factors that affect the evolution of the coastline; (2) a river branch appeared in the northern part of the Qingshuigou region in 2014 and became a major branch in 2020, which would affect the development of the coastal region of Chengdao. This study is important for better understanding the evolution pattern of the Yellow River Delta coastline and will help to provide guidance for coastline management and resource exploitation.