Land use change is the second largest source of greenhouse gas emissions after fossil combustion, which can hurt ecological environment severely. Intensive study on land use carbon emissions is of great significance to alleviate environmental pressure, formulate carbon emission reduction policy, and protect ecological development. The lower Yellow River area is an important area of economic development, grain cultivation, and agricultural production in China. Land use change has significant economic, environmental, and ecological impacts in this region. Deep study of land used carbon emissions and its influencing factors in the lower Yellow River area is not only of great significance to the environmental improvement in the Yellow River basin, but also can provide references for the research of other basins. Based on this, this paper studies the land use carbon emissions of 20 cities in the lower Yellow River area from 1995 to 2018. The results showed that from 1995 to 2018, the land use change was characterized by the decrease of the ecological land and the increase of the built-up land significantly. The overall carbon emission of the lower Yellow River area is increasing, and the built-up land is the main factor that leads to the increase of carbon emission, which can be also proven by the analysis of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The economic contributive coefficient (ECC) and ecological support coefficient (ESC) of carbon emission in the lower Yellow River area show a trend of high in Zhengzhou, Jinan, and Zibo and low in Zhoukou, Shangqiu, and Heze, and there was no significant changes during the study period, which indicates that each city did not achieve the coordinated development of the ecological economy. Finally, analysis results of the STIRPAT model indicated that the area of built-up land had the greatest impact on land use carbon emissions, followed by tertiary industry, whereas per capita gross domestic product (GDP) had the smallest impact. For every 1% increase in the area of built-up land, carbon emissions increased by 1.024%. By contrast, for every 1% increase in the contribution of tertiary industry to the GDP and per capita GDP, carbon emissions decreased by 0.051% and 0.034%, respectively. According to the study, there are still many problems in the coordinated development of economy and ecology in the lower Yellow River area. The lower Yellow River area should control the expansion of built-up land, afforestation, development of technology, reduction of carbon emissions, and promotion of the high-quality development of the Yellow River Basin.

Global warming has brought increased attention to the relationship between carbon emissions and economic development. Research on the driving factors of carbon emissions from energy consumption can provide a scientific basis for regional energy savings, as well as emissions reduction and sustainable development. Henan Province is a major agricultural province in China, and it is one of most populous provinces. Industrial development and population growth are the causes of carbon emissions. The STIRPAT model was conducted for analyzing carbon emissions and the driving factors for future carbon emission in Henan Province. The results show that: carbon emissions and energy consumption in Henan Province presented a rising trend from 1995 to 2014; Energy consumption due to population growth is the main contributor to carbon emissions in Henan Province. As every 1% increase in the population, GDP per-capita, energy intensity, and the level of urbanization development will contribute to the growth of emissions by 1.099, 0.193, 0.043, and 0.542%, respectively. The optimization of the industrial structure can reduce carbon emissions in Henan Province, as suggested by the results, when the tertiary sector increased by more than 1%, the total energy consumption of carbon emissions reduced by 1.297%. The future pattern of carbon emissions in Henan Province is predicted to increase initially and then follows by a decreasing trend, according to scenario analysis; and maintaining a low population growth rate, and a high growth rate of GDP per-capita and technical level is the best mode for social and economic development.

Climate variation and land use changes have been widely recognized as two major factors that impact hydrological processes. However, it is difficult to distinguish their contributions to changes in streamflow. Quantifying their contributions to alteration of streamflow is especially important for the sustainable management of water resources. In this study, the changes in streamflow for the period of 1960–2008 at two stations (Dongwan and Luhun) were analyzed in the Yihe watershed in China based on hydrological data series and climate parameters. Using a non-parametric Mann–Kendall (MK) and Pettitt’s test, as well as Budyko analysis, we first examined the trends of hydroclimatic variables and the breakpoint of annual streamflow over the past 50 years. Subsequently, we evaluated the contributions of annual precipitation (P), potential evapotranspiration (PET), and land use condition (represented by w), respectively, to streamflow variation. We observed a decreasing trend for P, as well as increasing trends for PET and w. Annual streamflow showed a significant downward trend with an abrupt change occurring in 1985 during the period of 1960–2008. Accordingly, we divided the studied period into two sub-periods: period I (1960–1985) and period II (1986–2008). The sensitivity of the streamflow to the different environmental factors concerned in this study differed. Streamflow was more sensitive to P than to PET and w. The decrease in P was the greatest contributor to the decline in streamflow, which accounted for 50.01% for Dongwan and 55.36% for Luhun, followed by PET, which accounted for 24.25% for Dongwan and 24.45% for Luhun, and land use change was responsible for 25.25% for Dongwan and 20.19% for Luhun. Although land use change plays a smaller role in streamflow reduction, land use optimization and adjustment still have great significance for future water resource management, since climate variation is difficult to control; however, the pattern optimization of land use can be achieved subjectively.

Clarifying the development relationship between urban built-up land and economic hinterland can provide decision support for regional sustainable development. Using the improved field model, geographic concentration and elasticity coefficient, and taking the lower reaches of the Yellow River as the study area, this study defines the scope of urban economic hinterland in the lower reaches of the Yellow River in China from 2005 to 2017, further analyzes the dynamic process of urban built-up land agglomeration and economic hinterland agglomeration, and reveals the spatial–temporal coupling relationships of urban built-up land expansion and economic hinterland development. The results revealed the following: (1) From 2005 to 2017, the spatial and temporal change pattern of the economic hinterland of each city in the lower reaches of the Yellow River had basically the same trend, showing a low degree of coincidence with the administrative divisions and obvious differences in scope and change; (2) The geographic concentration of built-up land showed a trend of centering on the cities of Zhengzhou and Jinan, spreading to the periphery, and gradually forming a high-value contiguous area in both cities. The spatial distribution patterns of the geographic concentration of economic hinterland are mainly manifested in Zhengzhou and Jinan, showing a circle structure of areas with “highest-low-higher” concentrations of economic hinterland moving away from the urban center; (3) The spatial–temporal coupling between the expansion of urban built-up land and the development of economic hinterland underwent an obvious transformation process. From 2005 to 2011, the coupling mode was mainly growth, and, from 2011 to 2017, it began to shift to extensive and intensive development. The coupling model of urban built-up land and economic hinterland in the lower reaches of the Yellow River has a good trend.