Soil salinization is a global problem that limits agricultural development and impacts human life. This study aimed to understand the dynamic changes in water and salinity in saline-alkali soil based on an indoor soil column simulation. We studied the changes in the water and salt contents of soils with different degrees of salinization under various irrigation conditions. The results showed that after seven irrigations, the pH, conductivity and total soluble salt content of the percolation samples after irrigation generally increased initially then decreased with repeated irrigation. The soil moisture did not change significantly after irrigation. The pH, conductivity, and total soluble salt content of each layer of the soil profile exhibited general declining trends. In the soil profile from Changguo Township (CG), the pH decreased from 8.21–8.35 to 7.71–7.88, the conductivity decreased from 0.95–1.14 ms/cm to 0.45–0.68 ms/cm, and the total soluble salt content decreased from 2.63–2.81 g/kg to 2.28–2.51 g/kg. In the soil profile from Zhongjie Industrial Park (ZJ), the pH decreased from 8.36–8.54 to 7.73–7.96, the conductivity decreased from 1.58–1.68 ms/cm to 1.45–1.54 ms/cm, and the total soluble salt decreased from 2.81–4.03 g/kg to 2.56–3.28 g/kg. The transported salt ions were primarily K⁺, Na⁺ and Cl^-. After several irrigations, a representative desalination effect was achieved. The results of this study can provide technical guidance for the comprehensive management of saline-alkali soils.

The ecological compensation mechanism is a tool for managing regional development and promoting the green economy. This paper proposes a revised model for carbon emissions and absorption based on IPCC, then analyses the spatial and temporal variations of carbon emissions, absorption, and deficit in 31 provinces of China from 2001 to 2019. A model was developed to redistribute carbon deficits and compensate for ecological imbalances, with the aim of eliminating background differences between regions. The concept of ecological compensation sensitivity was proposed, and a redundancy analysis (RDA) was conducted to detect possible influencing factors. Results indicate that: (1) The carbon deficits were relatively substantial in the centre and developed coastal regions, which were the subject regions of China’s ecological compensation. Meanwhile, the northwest and southwest regions were the object regions of ecological compensation, and the compensated object regions tended to extend towards the southeast. The majority of compensated subject areas are generally less sensitive than compensated object regions. (2) The graph of carbon deficit volume over time shows that China’s two carbon targets are closely aligned. The distribution of China’s ecological compensation amounts follows the pattern of provincial economic development levels, with higher compensation amounts concentrated in the central and coastal regions. There is a positive correlation between GDP, year, and population size with carbon emission and carbon deficit. Additionally, there is a positive correlation between year, region, and carbon absorption. (3) This compensation model can strongly incentivise the compensation subject area to proactively adjust its economic development model to cope with the significant compensation pressure. Additionally, it can fully encourage the compensation recipient area to continue adhering to the green economic development model. The conclusions of the study hold significant reference value for promoting the development of a green, low-carbon economy.

Land is an important part of our living environment. As far as China is concerned, the research on Sustainable Utilization of land resources is relatively late. At present, there is not a complete set of principles of land resource evaluation index system in China. The relevant research is more theoretical, and the actual application cases are less. In the context of the urgent need for development of the national economy, it is an urgent problem to improve the sustainable utilization rate of land resources, which will directly affect the structural reform and the overall strategy of our country. Therefore, based on the big data of the Internet of things, combined with RS and GIS and other related intelligent technologies, this paper establishes a set of evaluation model for the sustainable use of land resources. The method in this paper is to establish the index evaluation system first, then carry out data preprocessing through GIS and RS technology, and finally use big data technology for data mining. According to the actual situation of land resources in China, Delphi method is used as the main qualitative technical analysis method to set a reasonable weight for the evaluation index system of land resources. In order to verify the effect of this method, when analyzing the land resource data of a county from 2009 to 2018, this paper carried out four experiments including the overall trend analysis of land sustainable utilization. The experimental data results show that the use of big data in this paper has achieved ideal results in the application of land resource sustainable utilization, and to a certain extent, it fills in the big data and land The case study in the field of sustainable utilization of resources is a good method and has a broad application prospect.

The Taihangshan-Yanshan region (TYR) is an important ecological barrier area for Beijing-Tianjin-Hebei, and the effectiveness of its ecological restoration and protection is of great significance to the ecological security pattern of North China. Based on the FVC data from 2000 to 2021, residual analysis, parametric optimal geodetector technique (OPGD) and multi-scale geographically weighted regression analysis (MGWR) were used to clarify the the multivariate driving mechanism of the evolution of FVC in the TYR. Results show that: (1) FVC changes in the TYR show a slowly fluctuating upward trend, with an average growth rate of 0.02/10a, and a spatial pattern of "high in the northwest and low in the southeast"; more than half of the FVC increased during the 22-year period. (2) The results of residual analysis showed that the effects of temperature and precipitation on FVC were very limited, and a considerable proportion (80.80% and 76.78%) of the improved and degraded areas were influenced by other factors. (3) The results of OPGD showed that the main influencing factors of the spatial differentiation of FVC included evapotranspiration, surface temperature, land use type, nighttime light intensity, soil type, and vegetation type (q > 0.2); The explanatory rates of the two-factor interactions were greater than those of the single factor, which showed either nonlinear enhancement or bifactorial enhancement, among which, the interaction of evapotranspiration with mean air and surface temperature has the strongest effect on the spatial and temporal evolution of FVC (q = 0.75). Surface temperature between 4.98 and 10.4 °C, evapotranspiration between 638 and 762 mm/a, and nighttime light between 1.96 and 7.78 lm/m² favoured an increase in vegetation cover, and vegetation developed on lysimetric soils was more inclined to be of high cover. (4) The correlation between each variable and FVC showed different performance, GDP, elevation, slope and FVC showed significant positive correlation in most regions, while population size, urban population proportion, GDP proportion of primary and secondary industries, and nighttime light intensity all showed negative correlation with FVC to different degrees. The results can provide data for formulating regional environmental protection and restoration policies.

With the development of global economy, the sustainable use of land resources has become a key research topic in the current social and economic development. To do a good job in the sustainable use of land resources can solve such problems as waste of land resources, quality degradation, environmental damage, ecological imbalance and so on. How to realize the sustainable utilization of resources, the first thing to be solved is the sustainable monitoring of land resources. At present, many researches have been done in this field at home and abroad, but in conclusion, there are some problems, that is, the basic theory is not deep and the practical application is less. Therefore, this paper will study the application of the monitoring system of the sustainable use of soil and land resources under the support of the Internet of things big data technology, and establish a practical monitoring system of the sustainable use of land resources. The monitoring system in this paper makes full use of modern science and technology. On the basis of intelligent technology, combined with the actual situation of land resources in China, according to the practical problems, the construction principles of evaluation index system and index construction are established. In the aspect of monitoring system construction, we have greatly optimized the traditional construction method, which makes the system in this paper have better adaptability, scalability and accuracy. In order to further verify the reliability of the system, this paper carries out the performance test, accuracy test, and monitoring test in extremely harsh environment. Finally, the data show that the system can be applied to most of the land environment to monitor the sustainability of land resources.

The contradiction between rapid urbanization and ecological protection leads to the aggravation of carbon sink function differentiation in coastal areas. In this study, a framework of "historical assessment-future prediction-mechanism analysis" was constructed, and InVEST, PLUS, SEM, and OPGD models were integratedto study the dynamics of carbon storage and its driving mechanisms in three rapidly urbanizing coastal areas (Beidaihe, Caofeidian and Huanghua) in the Bohai Rim region of China. The main results showed that: (1) carbon storage showed the characteristics of periodic change—the expansion of low-carbon land led to the decline of carbon storage (2000–2010), and then Caofeidian achieved 1.50 × 106 tons of carbon storage restoration through ecological restoration (2010–2020). (2) Carbon stocks are expected to increase in all four scenarios in 2030, with the largest increase in the Cultivated Land Protection (CP) scenario, with a peak of 3.00 × 107 tons. (3)The analysis of the driving mechanism showed that population density was the main driving factor (q = 0.2126–0.2129), and there was a significant nonlinear interaction with socio-economic factors, which jointly shaped the spatial pattern. This analysis reveals the spatiotemporal heterogeneity and core driving factors of carbon storage changes in coastal areas, emphasizes the key role of human activities under urbanization and ecological conflicts, and provides a scientific basis for the implementation of differentiated low-carbon spatial planning in rapidly developing coastal areas. Integrated InVEST-PLUS-OPGD-SEM framework reveals spatiotemporal carbon stock dynamics. CP scenario achieves highest carbon stock increase by 2030 in multi-scenario simulations. Population density identified as dominant driver synergizing with GDP/transport factors. Coastal wetland restoration in Caofeidian offsets urbanization-driven carbon losses.