Global climate change is likely to affect reference evapotranspiration (ET₀), which is important for agricultural water resources and food security. Analysis of ET₀ changes is also important for understanding climate change and its impacts on hydrology. In this study, ET₀ was calculated from 2020 to 2099 in nine agricultural regions and the entire country of China utilizing the Penman–Monteith method based on bias‐corrected climate data from the global climate models MIROC‐ESM‐CHEM, NorES1‐M, IPSL‐CM5A‐LR, GFDL‐ESM 2M and HadGEM2‐ES under future climate change scenarios. The spatiotemporal characteristics of seasonal and annual ET₀ and climatic variables were analysed based on the Mann–Kendall test with trend‐free prewhitening (TFPW‐MK) and Sen's slope estimator. Sensitivity coefficient and multivariate regression were used to identify factors controlling the change in ET₀. The results showed that seasonal and annual air temperature and precipitation both increased under all climate scenarios, and the majority of relative humidity and wind speed values showed downward trends during 2020–2099. The lowest annual ET₀ values were found in the NECR and QTR, and the SCR had the maximum ET₀. Both the TFPW‐MK and slope statistics showed an upward trend of seasonal ET₀ during 2020–2059 under all scenarios. During 2060–2099, seasonal ET₀ values showed a slight and nonsignificant downward trend under RCP 2.6, while upward trends were produced in other scenarios. Areas with high ET₀ values were located in the SCR, GXR and HHHR, while low ET₀ values mainly occurred in the NECR and QTR. The ET₀ for the four seasons in the SCR was higher than that in the other regions, and the summer ET₀ was the highest compared with that in the other seasons overall. For the attribution assessment, T_avg is the main controlling variables to ET₀ trends under the interaction of multiple climatic factors though RH is the most sensitive.

Improving carbon efficiency and reducing carbon intensity are effective means of mitigating climate change. Carbon emissions due to urban residential energy consumption have increased significantly; however, there is a lack of research on urban residential carbon intensity. This paper examines the spatiotemporal variation of carbon intensity in the residential sector during 2001–2015, and then identifies the causes of the variation by utilizing the logarithmic mean Divisia index (LMDI) with the help of Microsoft Excel 2016 for 620 county-level cities in 30 Chinese provinces. The results show that high carbon intensity is mainly found in large cities, such as Beijing, Tianjin, and Shanghai. However, these cities showed a downward trend in carbon intensity. In terms of influencing factors, the energy consumption per capita, urban sprawl, and land demand are the three most influential factors in determining the changes in carbon intensity. The effect of energy consumption per capita mainly increases the carbon intensity, and its impact is higher in the municipal districts of provincial capital cities than in other types of cities. Similarly, the urban sprawl effect also promotes increases in carbon intensity, and a higher degree of influence appears in large cities. However, as urban expansion plateaus, the effect of urban sprawl decreases. The land-demand effect reduces the carbon intensity, and the degree of influence of the land-demand effect on carbon intensity is also clearly stronger in big cities. Our findings show that lowering the energy consumption per capita and optimizing the land-use structure are a reasonable direction of efforts, and the effects of differences in influencing factors should be paid more attention to reduce carbon intensity.

Research Highlights: Our findings highlight that the contribution of carbon sequestration from plantations to REDD+ will remain limited, and that opportunity costs in Southeast Asia will likely increase, due to future oil palm expansion. Background and Objectives: Land use, land-use change, and forestry (LULUCF) are significant sources of carbon emissions. The United Nations Framework Convention on Climate Change (UNFCCC) agreed that the Reducing Emissions from Deforestation and Forest Degradation Plus program, also known as REDD+, could contribute to carbon sinks in tropical regions. These reductions could serve as carbon credits that offset emissions from other sources. Materials and Methods: This study uses the cellular automaton technique to simulate the business-as-usual (BAU) scenario and the gain-loss method, to measure carbon emissions resulting from forest conversion. The output of the integration of the models makes it possible to evaluate one of the most important financial costs: opportunity costs. Two scenarios (with and without consideration of carbon sequestration) in rubber and oil palm plantations are examined. Results: A sensitivity assessment in Kalimantan, Indonesia, shows that carbon sequestration from plantations affects value of opportunity costs less than social discount rates. Further analysis suggests that oil palm plantations have a greater impact than rubber plantations. Conclusions: Our study provides a case that can be applied to other regions for evaluating the impacts of plantation carbon sequestration, and insights that can help local policymakers design a financially attractive REDD+ program in other forest areas of the world.

Water resources are indispensable for all social-economic activities and ecosystem functions. In addition, changes in water resources have great significance for agricultural production. This paper uses five global climate models from CMIP5 to evaluate the future spatiotemporal variation in water resources in China under four RCP scenarios. The results show that the available precipitation significantly decreases due to evapotranspiration. Comparing the four RCP scenarios, the national average of the available precipitation is the highest under the RCP 2.6 and 4.5 scenarios, followed by that under the RCP 8.5 scenario. In terms of spatial distribution, the amount of available precipitation shows a decreasing trend from southeast to northwest. Regarding temporal changes, the available precipitation under RCP 8.5 exhibits a trend of first increasing and then decreasing, while the available precipitation under the RCP 6.0 scenario exhibits a trend of first decreasing and then increasing. Under the RCP 2.6 and 4.5 scenarios, the available precipitation increases, and the RCP 4.5 scenario has a higher rate of increase than that of RCP 2.6. In the context of climate change, changes in water resources and temperature cause widespread increases in potential agricultural productivity around Hu’s line, especially in southwestern China. However, the potential agricultural productivity decreases in a large area of southeastern China. Hu’s line has a partial breakthrough in the locking of agriculture, mainly in eastern Tibet, western Sichuan, northern Yunnan and northwestern Inner Mongolia. The results provide a reference for the management and deployment of future water resources and can aid in agricultural production in China.

Global climate change has led to more frequent occurrences of extreme, dangerous events; therefore, it is urgent to enhance cities’ adaptation to climate change. Focusing on the impact of high temperature, low temperature, drought, and flooding, we established multi-dimensional assessment systems covering natural, economic, and social elements for cities’ climate change adaptations. Based on the assessment systems, studies on adaptation to climate change were conducted in 248 cities in China using the entropy weight method, coefficient of variation method, and exploratory spatial data analysis; with the help of geographically weighted regression (GWR), the relationships between four types of urbanization and cities’ adaptation to climate events were explored. The results showed the following: (1) High-administrative-level cities had higher adaptation than ordinary prefecture-level cities. (2) The differences in adaptation to the four types of climate events between cities within each of the seven regions in China presented significantly different spatial patterns. (3) Under the four types of climate events, the global spatial correlations of cities’ adaptations in China were positive. The agglomeration characteristics of adaptation were mainly H–H and L–L agglomerations. (4) When analyzing the impacts of four types of urbanization on cities’ adaptation to climate events, the fitting effects of GWR models were far better than those of OLS models. Population urbanization, economic urbanization, land urbanization, and industrial urbanization had different impacts on adaptation. Under the influence of social and economic development, the urbanization regression coefficients of different cities had significant spatial differences.

Clarifying the influencing mechanism of rural revitalization on agricultural carbon emissions is crucial for attaining carbon peaking and carbon neutrality. This study utilized spatial econometric model, mediating effect model and dual fixed effect model to explore the influence and spatial impact of rural revitalization on carbon emissions from agricultural land by using the panel data of prefectural cities in Henan Province. Results indicate that rural revitalization exerts a notable beneficial influence on carbon emissions, as its improvement results in a rise in such emissions. Furthermore, rural revitalization demonstrates a favorable spatial spillover effect on agricultural carbon emissions in neighboring cities. Agricultural GDP and mechanical technological progress act as intermediate factors, as rural revitalization promotes carbon emissions from agriculture by fostering economic development and technological advancements. Heterogeneity analysis indicates that the correlation between rural revitalization and greenhouse gas emissions from agriculture is nonlinear, as moderate and low levels of rural revitalization promote agricultural carbon emissions, while higher levels exhibit a negative effect. Thus, rural revitalization exhibits an inflection point effect on agricultural carbon emissions.