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As a fundamental factor driving economic growth, natural capital plays a key role in sustainable development. This paper incorporates various economic growth proxy variables into the MRW model and uses the BMA method to tackle the uncertainty issue of the model. Based on China's provincial-level panel data, this paper examines the impact of renewable natural capital and non-renewable natural capital on the country's economic growth and then carries out multiple mechanism analysis with the CART algorithm. The main findings are: 1 Natural capital has a significant deep impact on China's economic growth in models incorporating proximate and fundamental theories, and the direction of natural capital's impact varies with proxy indicators, which shows a significant 'Curse' effect of dependence and a 'Blessing' effect of abundance. This panoramic perspective can reconcile the serious disagreement on 'Resource Curse' among the literature. 2 The effects of renewable natural capital are in line with the 'Resource neutral' hypothesis, while nonrenewable natural capital has a stronger impact on economic growth in terms of both the 'Curse' effect of dependence and the 'Blessing' effect of abundance, and the overall effect on economic growth depends on patterns of resource use. 3 Under high physical capital accumulation, natural capital has a more fundamental impact on economic growth, and the 'Blessing' effect of abundance has been significantly enhanced. Based on their resource endowments and economic development levels, all provinces should reinvest natural resource rents in national wealth to mitigate the 'Resource Curse' through effective protection of natural capital and compensation from reinvestment. This is a reasonable way to embark on a win-win sustainable development path of economic development and ecological civilization. [ABSTRACT FROM AUTHOR]

Accurate prediction of regional water evapotranspiration can greatly contribute to the rational utilization of regional water resources for the water resources saving. Crop evapotranspiration can be one of the most important indicators for the water evapotranspiration status of crops, in order to evaluate the soil water balance of farmland and the water management of farmland. However, the calculation of crop evapotranspiration requires a large amount of meteorological factor data. There is often redundant data with the low correlation with the evapotranspiration in meteorological data, which seriously affects the prediction accuracy and efficiency of the model. It is a high demand to extract the core environmental factors for the less-factor prediction model. The classification and regression tree (CART) can be used to make feasible for the large data sources in a short time. Then, Less factors can be extracted for the predictive analysis in a more scientific way. In this study, a less-factor water evapotranspiration model was constructed to efficiently and accurately predict the evapotranspiration data. The core factors were selected from multiple meteorological factors. 23 typical stations were selected in the nine agricultural regions, and then collected data on eight meteorological factors, such as precipitation, and sunshine hours. The meteorological factors were ranked in the order of importance using CART. The top 3-5 meteorological factors were then selected to predict the evapotranspiration using the extreme learning machine (ELM) model. At the same time, the ELM model was optimized using genetic algorithm (GA), particle swarm optimization (PSO), sparrow search algorithm (SSA). Three optimization algorithms (GA-ELM, PSO-ELM, SSA-ELM) were used to construct a less-factor hybrid optimization water evapotranspiration prediction model. Relative root mean square error (RMSE), coefficient of determination (R 2 ), mean absolute error (MAE), and NashSutcliffe coefficient (NSE) were used to evaluate the performance of the ELM and optimization model. The results showed that: 1) The main influencing factors were ranked in the order of the precipitation, sunshine duration, mean station pressure, daily maximum temperature, and average relative humidity using the importance ranking of the CART algorithm. 2) The PSOELM model presented the highest prediction accuracy among the three optimization algorithms. Specifically, the RMSE of evapotranspiration prediction for the 23 stations was ranged from 6.608 to 22.077 mm/d, the NSE of 0.824-0.998, R 2 of 0.908- 0.995, and MAE of 5.075-16.677 mm/d. In addition, the RF and SVR model with the strong generalization performance were selected to compare with the PSO-ELM model. The prediction performance of three models was slightly improved with the increase of the input factors, indicating the slight overall improvement. The three models shared the strong generalization and robustness. The meteorological factors were input into the prediction model, according to the order of importance. The meteorological factors ranked the 4th and 5th were relatively less important, where the prediction accuracy was slightly improved with the increase of the input parameters. The PSO-ELM model presented the highest prediction accuracy among the three models. 3) The ELM model performed better applicability in the Yunnan-Guizhou Plateau region, the Sichuan Basin, and the surrounding areas. The three optimization algorithms showed the better applicability in the South China and the YunnanGuizhou Plateau region, with the highest applicability of the PSO-ELM model. The findings can provide an important reference for the crop water demand calculation in nine major agricultural regions in China. [ABSTRACT FROM AUTHOR]