An explicit spatial carbon emission map is of great significance for reducing carbon emissions through urban planning. Previous studies have proved that, at the city scale, the vector carbon emission maps can provide more accurate spatial carbon emission estimates than gridded maps. To draw a vector carbon emission map, the spatial allocation of greenhouse gas (GHG) inventory is crucial. However, the previous methods did not consider different carbon sources and their influencing factors. This study proposes a point-line-area (P-L-A) classified allocation method for drawing a vector carbon emission map. The method has been applied in Changxing, a representative small city in China. The results show that the carbon emission map can help identify the key carbon reduction regions. The emission map of Changxing shows that high-intensity areas are concentrated in four industrial towns (accounting for about 80%) and the central city. The results also reflect the different carbon emission intensity of detailed land-use types. By comparison with other research methods, the accuracy of this method was proved. The method establishes the relationship between the GHG inventory and the basic spatial objects to conduct a vector carbon emission map, which can better serve the government to formulate carbon reduction strategies and provide support for low-carbon planning.

The key to coping with climate change is to control carbon emissions from energy consumption. Scientific prediction of energy consumption carbon emissions based on influencing factors is of great significance to the determination of carbon control aim and emission reduction strategies. Given the lack of previous studies on county-level carbon emissions, this paper proposed a systematic approach to study the influencing factors of county-level energy consumption carbon emissions and to predict future emissions. Firstly, the annual energy consumption carbon emissions were calculated based on the method proposed by the Intergovernmental Panel on Climate Change (IPCC). Then the expanded Kaya equation and existing research were combined to select influencing factors for the establishment of the optimal Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, which was used to quantitatively analyze the influencing factors of carbon emissions from energy consumption at the county level. Finally, the emission reduction aims and low-carbon strategies were determined based on scenario analysis. The method was applied to Changxing, a typical county with large energy consumption and carbon emissions. Based on 16 years of data, the STIRPAT carbon emission prediction model was established and the forecast results of future emissions under three different scenarios were obtained. The results indicated that population size, industrial structure, and affluence degree were the three most influential factors, and the influence degree of each factor was quantified to support targeted low-carbon strategies for county-level cities.

Generative design based on machine learning has become an important area of application for artificial intelligence. Regarding the generative design process for residential site plan layouts (hereafter referred to as “RSPLs”), the lack of experimental demonstration begs the question: what are the design preferences of machine learning? In this case, all design elements of the target object need to be extracted as much as possible to conduct experimental studies to produce scientific experimental results. Based on this, the Pix2pix model was used as the test case for Chinese residential areas in this study. An experimental framework of “extract-translate-machine-learning-evaluate” is proposed, combining different machine and manual computations, as well as quantitative and qualitative evaluation techniques, to jointly determine which design elements and their characteristic representations are machine learning design preferences in the field of RSPL. The results show that machine learning can assist in optimizing the design of two particular RSPL elements to conform to residential site layout plans: plaza paving and landscaped green space. In addition, two other major elements, public facilities and spatial structures, were also found to exhibit more significant design preferences, with the largest percentage increase in the number of changes required after machine learning. Finally, the experimental framework established in this study compensates for the lack of consideration that all design elements of a residential area simultaneously utilize the same methodological framework. This can also assist planners in developing solutions that better meet the expectations of residents and can clarify the potential and advantageous directions for the application of machine learning-assisted RSPL.

Higher Education Institutions (HEIs) play an increasingly significant role in the practice of sustainability. For HEIs in their early stages of sustainability, they are still in need of sustainable assessment tools (SATs) that are suitable for their local context and also lead international sustainable development. The purpose of this paper is to develop a two-hierarchy sustainability assessment tool (THSus) for Chinese higher education institutions, including a quick analysis tool (QAT) and an in-depth benchmarking tool (IBT). The QAT provided a general overview of campus sustainability for HEIs to initiate initial actions and screen cases for the IBT. The IBT then provides more targeted analysis to plan long-term strategic changes. Based on the analysis of HEI cases, a 34-person Chinese research team was enlisted to discuss and select characteristics to formulate THSus. Indicators and weightings were developed according to the tool’s purpose and applied to 15 cases to test its effectiveness. Results showed that THSus is suitable for systematically analyzing campus issues, particularly in research areas. It offers a regional solution for Chinese campuses that is adaptable and considers the comprehensive core of sustainability.