Diverse urban environmental elements provide health and amenity value for residents. People are willing to pay a premium for a better environment. Thus, it is essential to assess the benefits and values of these environmental elements. However, limited by the interpretability of the machine learning model, existing studies cannot fully excavate the complex nonlinear relationships between housing prices and environmental elements, as well as the spatial variations of impacts of urban environmental elements on housing prices. This study explored the impacts of urban environmental elements on residential housing prices based on multisource data in Shanghai. A SHapley Additive exPlanations (SHAP) method was introduced to explain the impacts of urban environmental elements on housing prices. By combining the ensemble learning model and SHAP, the contributions of environmental characteristics derived from street view data and remote sensing data were computed and mapped. The experimental results show that all the urban environmental characteristics account for 16 percent of housing prices in Shanghai. The relationships between housing prices and two green characteristics (green view index from street view data and urban green coverage rate from remote sensing) are both nonlinear. Shanghai’s homebuyers are willing to pay a premium for green only when the green view index or urban green coverage rate are of higher value. However, there are significant differences between the impacts of the green view index and urban green coverage rate on housing prices. The sky view index has a negative influence on housing prices, which is probably because the high-density and high-rise residential area often has better living facilities. Residents in Shanghai are willing to pay a premium for high urban water coverage. The case of Shanghai shows that the proposed framework is practical and efficient. This framework is believed to provide a tool to inform the decisions of housing buyers, property developers and policies concerning land-selling and buying, property development and urban environment improvement.

Forests offer significant climate mitigation benefits, but existing emissions reduction assessment methodologies in forest-based mitigation activities are not scalable, which limits the development of carbon offset markets. In this study, we propose a measurement method using optical satellite imagery and space LiDAR data fusion to assess forest emissions reduction. Compared with the ALS-based carbon stock density estimation method, our approach presented a strong scalability for mapping 10 m-resolution carbon stock at a large scale. It was observed that dense canopy top height estimated by combining GEDI and Sentinel-2 could accurately predict forest carbon stock measurements estimated by the ALS-based method ($R^2$ = 0.72). By conducting an on-site experiment of an ongoing forest carbon project in China, we found the consistency between the emissions reduction assessed by the data fusion measurement method (589,169 ${\mathrm{tCO}}_2\mathrm{e}$) and the official ex post-monitored emissions reduction in the monitoring report (598,442 ${\mathrm{tCO}}_2\mathrm{e}$). Our results demonstrated that forest carton stock estimation using optical satellite imagery and space LiDAR data fusion is efficient and economical for forest emissions reduction assessment. The acquisition of the data was more efficient over large areas with high frequencies using space-based technology. We further discussed the challenge of building a near-real-time monitoring system for forest-based mitigation activities by utilizing optical satellite imagery and space LiDAR data and pointed out that a quality control framework should be established to help us understand the sources of uncertainty in LiDAR-based models and improve carbon stock estimation from individual trees to forest carbon projects to meet the requirements of carbon standards better.

Spatial co-location pattern mining is employed to identify a group of spatial types whose instances are frequently located in spatial proximity. Current co-location mining methods have two limitations: (1) it is difficult to set an appropriate proximity threshold to identify close instances in an unknown region, and (2) such methods neglect the effects of the distance values between instances and long-distance instance effects on pattern significance. This paper proposes a novel maximal co-location algorithm to address these problems. To remove the first constraint, the algorithm uses Voronoi diagrams to extract the most related instance pairs of different types and their normalized distances, from which two distance-separating parameters are adaptively extracted using a statistical method. To remove the second constraint, the algorithm employs a reward-based verification based on distance-separating parameters to identify the prevalent patterns. Our experiments with both synthetic data and real data from Beijing, China, demonstrate that the algorithm can identify many interesting patterns that are neglected by traditional co-location methods.