Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the scientific community aiming to gain new insights into the temporal and spatial variation of different carbon fluxes in terrestrial ecosystems. In this study, adaptive neuro-fuzzy inference system (ANFIS) and generalized regression neural network (GRNN) models were developed to predict the daily carbon fluxes in three boreal forest ecosystems based on eddy covariance (EC) measurements. Moreover, a comparison was made between the modeled values derived from these models and those of traditional artificial neural network (ANN) and support vector machine (SVM) models. These models were also compared with multiple linear regression (MLR). Several statistical indicators, including coefficient of determination (R²), Nash-Sutcliffe efficiency (NSE), bias error (Bias) and root mean square error (RMSE) were utilized to evaluate the performance of the applied models. The results showed that the developed machine learning models were able to account for the most variance in the carbon fluxes at both daily and hourly time scales in the three stands and they consistently and substantially outperformed the MLR model for both daily and hourly carbon flux estimates. It was demonstrated that the ANFIS and ANN models provided similar estimates in the testing period with an approximate value of R² = 0.93, NSE = 0.91, Bias = 0.11 g C m⁻² day⁻¹ and RMSE = 1.04 g C m⁻² day⁻¹ for daily gross primary productivity, 0.94, 0.82, 0.24 g C m⁻² day⁻¹ and 0.72 g C m⁻² day⁻¹ for daily ecosystem respiration, and 0.79, 0.75, 0.14 g C m⁻² day⁻¹ and 0.89 g C m⁻² day⁻¹ for daily net ecosystem exchange, and slightly outperformed the GRNN and SVM models. In practical terms, however, the newly developed models (ANFIS and GRNN) are more robust and flexible, and have less parameters needed for selection and optimization in comparison with traditional ANN and SVM models. Consequently, they can be used as valuable tools to estimate forest carbon fluxes and fill the missing carbon flux data during the long-term EC measurements.

CO₂ emissions from fossil energy have caused global climate problems and threatened human survival. However, there are few studies on the spatiotemporal distribution and driving factors of carbon emissions. This paper takes the Yangtze River Delta (YRD) urban agglomeration as the research object and analyzes the spatiotemporal heterogeneity of carbon dioxide emissions and their driving factors from 2000 to 2017. First, a series of preprocessing, such as resample, interpolation, and image clipping, are conducted on the CO₂ emission data and nighttime light remote sensing images. Second, the dynamic time wrapping (DTW) and hierarchical clustering algorithms were involved in manipulating the CO₂ emission data. Consequently, the cities’ and CO₂ emissions’ time series were classified into four categories and three stages separately. Finally, the geographical detector model (GDM) and geographical and temporal weighted regression (GTWR) are coupled to evaluate the spatiotemporal heterogeneity and quantify the driving factors. The results show the following: (1) The spatiotemporal distribution of CO₂ emissions has spatial consistency from 2000 to 2017. High-emission areas are concentrated in economically developed areas such as Shanghai, Suzhou, and Wuxi. The results are consistent with previous research. (2) Regional aggregation is a revealed new trend. CO₂ emissions in the target urban areas are gradually converging into economic center cities and diverse class cities, e.g., Shanghai and Ningbo. (3) In cities of different economic development levels, the driving factors of CO₂ emissions are different. The secondary sector and urban infrastructure dominate in the early stages of developed cities. On top of that, the influence of the tertiary industry is more significant in the later development stages. According to the results, in the urban development process, humans should not only pursue the increase in speed but also pay attention to the negative impact of the economic development process on the ecological environment. Besides, since the spatiotemporal characteristics and dominant factors of urban carbon emissions are different in each stage of development, the formulation of carbon reduction policies should be associated with urban features.

In coalbed methane (CBM) development, it is imperative to evaluate the reservoir’s potential and identify the favorable areas to achieve higher production. This paper presents a novel quantified model inspired by the reservoir hydrodynamic heterogeneity. Treating the groundwater hydrodynamic zones as the computation units, the maximal information coefficient (MIC) and the geodetector model (GDM) were applied to measure the correlation between reservoir parameter and average daily gas production. An evaluation index system was then established, and its weight vector was obtained from a game theory model which couples the MIC scores with the GDM q-values. Taking the Gujiao mining area in the northern Qinshui Basin as a case study, the proposed model demonstrates a reliable classification of reservoir potential in alignment with the prior studies. The result indicates that the proposed model provides an insight into the relationship between CBM production and the reservoir heterogeneity, e.g., the distinction of hydrodynamics leads to the importance variation of the primary reservoir parameters and results in the uneven CBM production. Moreover, the model offers a quantified measurement of the correlation between reservoir heterogeneity and CBM production, and eliminates the subjective influence from the conventional methods.