Evaluating nighttime lights and population distribution as proxies for mappinganthropogenic CO2 emission in Vietnam, Cambodia and Laos

Spatialization and driving factors of carbon budget at county level in the Yangtze River Delta of China

The county is the basic administrative unit of China, and the spatialization of carbon budget at the county scale plays an irreplaceable role in deepening the understanding of the carbon emission mechanism and spatial pattern. Yueqing County, an economically developed county in the Yangtze River Delta of China, was selected as the study area, the spatial pattern of the carbon budget and the optimal resolution of the spatialization at the county level were dissected on the basis of accurate accounting, and driving factors of carbon emissions were further identified using the geographically weighted regression model. The results indicated that (1) the carbon emissions were mainly generated from fossil fuel combustion related to energy, accounting for 98.8% of the total carbon budget in the study area; (2) the optimal resolution of spatialization was 200 m and carbon emissions were concentrated in the southeast of the study area; (3) energy intensity, energy structure, per capita GDP, and urbanization rate were positively correlated with carbon emissions, while population played a bidirectional role in carbon emissions. This study not only strengthens the understanding of the patterns and drivers of the carbon budget but also establishes a theoretical framework and operational tools for policymakers to formulate solutions to mitigate the carbon crisis.

Construction of the machine learning-based live birth prediction models for the first in vitro fertilization pregnant women

BACKGROUND

This study was to conduct prediction models based on parameters before and after the first cycle, respectively, to predict live births in women who received fresh or frozen in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) for the first time.

METHODS

This retrospective cohort study population consisted of 1,857 women undergoing the IVF cycle from 2019 to 2021 at Huizhou Municipal Central Hospital. The data between 2019 and 2020 were completely randomly divided into a training set and a validation set (8:2). The data from 2021 was used as the testing set, and the bootstrap validation was carried out by extracting 30% of the data for 200 times on the total data set. In the training set, variables are divided into those before the first cycle and after the first cycle. Then, predictive factors before the first cycle and after the first cycle were screened. Based on the predictive factors, four supervised machine learning algorithms were respectively considered to build the predictive models: logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM). The performances of the prediction models were evaluated by the area under the receiver operator characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy.

RESULTS

Totally, 851 women (45.83%) had a live birth. The LGBM model showed a robust performance in predicting live birth before the first cycle, with AUC being 0.678 [95% confidence interval (CI): 0.651 to 0.706] in the training set, 0.612 (95% CI: 0.553 to 0.670) in the validation set, 0.634 (95% CI: 0.511 to 0.758) in the testing set, and 0.670 (95% CI: 0.626 to 0.715) in the bootstrap validation. The AUC value in the training set, validation set, testing set, and bootstrap of LGBM to predict live birth after the first cycle was 0.841 (95% CI: 0.821 to 0.861), 0.816 (95% CI: 0.773 to 0.859), 0.835 (95% CI: 0.743 to 0.926), and 0.839 (95% CI: 0.806 to 0.871), respectively.

CONCLUSION

The LGBM model based on the predictive factors before and after the first cycle for live birth in women showed a good predictive performance. Therefore, it may assist fertility specialists and patients to adjust the appropriate treatment strategy.

Comparing a global high-resolution downscaled fossil fuel CO2 emission dataset to local inventory-based estimates over 14 global cities

BACKGROUND

Compilation of emission inventories (EIs) for cities is a whole new challenge to assess the subnational climate mitigation effort under the Paris Climate Agreement. Some cities have started compiling EIs, often following a global community protocol. However, EIs are often difficult to systematically examine because of the ways they were compiled (data collection and emission calculation) and reported (sector definition and direct vs consumption). In addition, such EI estimates are not readily applicable to objective evaluation using modeling and observations due to the lack of spatial emission extents. City emission estimates used in the science community are often based on downscaled gridded EIs, while the accuracy of the downscaled emissions at city level is not fully assessed.

RESULTS

This study attempts to assess the utility of the downscaled emissions at city level. We collected EIs from 14 major global cities and compare them to the estimates from a global high-resolution fossil fuel CO2 emission data product (ODIAC) commonly used in the science research community. We made necessary adjustments to the estimates to make our comparison as reasonable as possible. We found that the two methods produce very close area-wide emission estimates for Shanghai and Delhi (< 10% difference), and reach good consistency in half of the cities examined (< 30% difference). The ODIAC dataset exhibits a much higher emission compared to inventory estimates in Cape Town (+ 148%), Sao Paulo (+ 43%) and Beijing (+ 40%), possibly related to poor correlation between nightlight intensity with human activity, such as the high-emission and low-lighting industrial parks in developing countries. On the other hand, ODIAC shows lower estimates in Manhattan (- 62%), New York City (- 45%), Washington D.C. (- 42%) and Toronto (- 33%), all located in North America, which may be attributable to an underestimation of residential emissions from heating in ODIAC's nightlight-based approach, and an overestimation of emission from ground transportation in registered vehicles statistics of inventory estimates.

CONCLUSIONS

The relatively good agreement suggests that the ODIAC data product could potentially be used as a first source for prior estimate of city-level CO2 emission, which is valuable for atmosphere CO2 inversion modeling and comparing with satellite CO2 observations. Our compilation of in-boundary emission estimates for 14 cities contributes towards establishing an accurate inventory in-boundary global city carbon emission dataset, necessary for accountable local climate mitigation policies in the future.