A large proportion of the cultivated land in China has been used for non-grain production purposes. As food insecurity is worsening worldwide, this issue has attracted attention from the Chinese government. In order to curb this trend and to ensure food security, this paper explores the quantitative characteristics and spatial distribution of cultivated land used for non-grain purposes in Liyang City, Jiangsu Province, and discusses the clustering characteristics and mechanisms behind this based on spatial autocorrelation analysis and geographically weighted regression (GWR). The results show that most of the cultivated land in Liyang City has not been used for non-grain purposes, and the cultivated land reserve is abundant. Among all land types, irrigable land has the largest non-grain production rate of cultivated land. There is no significant spatial correlation of cultivated land for non-grain purposes in most towns in Liyang, among which Kunlun Street is in the High-High (HH) zone and Daibu Town in the Low-High (LH) zone. It is also found that the same factor has various impacts on the non-grain production of cultivated land in different towns, and the number of enterprises is the core factor that leads to the non-grain use of cultivated land in Liyang city. Low food prices lead some farmers to plant other crops with higher economic benefits, and also lead to the outflow of the rural labor force. This will not only accelerate the non-grain production of cultivated land, but also cause a large amount of cultivated land to be in a state of unmanned cultivation, further aggravating the proportion of non-grain production in cultivated land.

Research on resources and environmental carrying capacity (RECC) has been expanding since the early 20th century, and RECC has become a global concern and criterion for measuring regional sustainable development. Land-use planning (LUP) serves as a key tool of socioeconomic–ecological coordinated development and is deeply associated with RECC. In China, the newly established spatial planning system of 2019 identifies RECC assessment as the basis for spatial planning. However, after systematically reviewing the research history, conceptual evolution, and evaluation methods of RECC, we found that the existing approach of RECC has not addressed the impacts of stakeholders’ behavior on RECC, in other words, the governance perspective has not been sufficiently discussed. Further, research on the interaction between RECC and LUP has been far from sufficient, hampering our deep understanding of the roles of LUP in improving RECC. In order to fill this gap, a new framework is proposed to explain the formation mechanism of RECC combining the governance considerations based on the social–ecological system (SES) framework, which has made contributions to enrich the research perspective of RECC and its theoretical and methods system. In addition, the interaction path between RECC and LUP is constructed according to the new RECC framework and a policy toolbox for improving RECC, which will provide a comprehensive and systematic practical application path for improving RECC and promoting regional socioeconomic–ecological coordinated sustainable development. The conclusion part discusses the future research topics and limitations for RECC and LUP.

In this study, we create a high-resolution (1 km x 1 km) carbon emission spatially gridded dataset in Shanghai for 2010 to 2015 to help researchers understand the spatial pattern of urban CO₂ emissions and facilitate exploration of their driving forces. First, we conclude that high spatial agglomeration, CO₂ emissions centralized along the river and coastline, and a structure with three circular layers are the three notable temporal–spatial characteristics of Shanghai fossil fuel CO₂ emissions. Second, we find that large point sources are the leading factors that shaped the temporal–spatial characteristics of Shanghai CO₂ emission distributions. The changes of CO₂ emissions in each grid during 2010–2015 indicate that the energy-controlling policies of large point emission sources have had positive effects on CO₂ reduction since 2012. The changes suggest that targeted policies can have a disproportionate impact on urban emissions. Third, area sources bring more uncertainties to the forecasting of carbon emissions. We use the Geographical Detector method to identify these leading factors that influence CO₂ emissions emitted from area sources. We find that Shanghai’s circular layer structure, population density, and population activity intensity are the leading factors. This result implied that urban planning has a large impact on the distribution of urban CO₂ emissions. At last, we find that unbalanced development within the city will lead to different leading impact factors for each circular layer. Factors such as urban development intensity, traffic land, and industrial land have stronger power to determine CO₂ emissions in the areas outside the Outer Ring, while factors such as population density and population activity intensity have stronger impacts in the other two inner areas. This research demonstrates the potential utility of high-resolution carbon emission data to advance the integration of urban planning for the reduction of urban CO₂ emissions and provide information for policymakers to make targeted policies across different areas within the city.

This research constructs a 1 km × 1 km Shanghai energy consumption and carbon emission spatial grid through a bottom-up approach. First, we locate all energy consumption locations in Shanghai via GIS. Second, we calculate energy consumption and associated CO₂ emissions by energy type, by usage type, and by facilities. Finally, we use a spatial grid to represent the energy consumption and CO₂ emissions. The grid shows CO₂ emissions in Shanghai are highly spatially correlated with energy types and volumes of consumption. This research also finds out that high energy consumption and carbon emission locations in Shanghai display significant spatial aggregation. In 7209 spatial energy consumption cells, the top 10 grids of emissions account for 52.8% of total CO₂ emissions in Shanghai; the top 20 grids account for 64.5% and the top 50 grids account for 76.5%. The most critical point emission sources are coal-fired power plants and iron and steel plants. The most important line emission sources are the Yan’an Road and Inner Ring viaducts. The area emission sources that account for the most future-projected growth are commercial and residential natural gas. After this spatial analysis, this paper makes policy suggestions and solutions to conserve energy consumption and mitigate carbon emissions in Shanghai.

In the context of global food insecurity, a large amount of cultivated land in China has been occupied by aquaculture ponds, leading to a series of variations in the ecological environment. The Chinese government pays close attention to the problem. In order to achieve sustainable development and ensure the safety of China’s cultivated land, the paper uses Liyang City as an example to discuss the spatial characteristics of the expansion of aquaculture ponds through occupying cultivated lands and analyzes the variations in ecosystem service value and cultivated land function. The conclusions are as follows: (1) 2073.24 hectares of cultivated lands were occupied for expanding aquaculture ponds in Liyang from 2009 to 2019, and there was a small number of new aquaculture ponds in the ecological protection area, which shows that the aquaculture ponds in Liyang City are at the stage of disorderly expansion; (2) the total value of ecosystem services increased by 1.43%; supply and support services values decreased, but the increase in regulation and cultural services values was sufficient to more than compensate for the mentioned losses; and (3) the expansion of aquaculture ponds leads to a decrease in the carbon storage of cultivated land, which in turn has negative impacts such as an increase in atmospheric carbon concentration.