Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate models

Wind energy development in Central Asia can help alleviate drought and fragile ecosystems.Nevertheless,current studies mainly used the global climate models(GCMs)to project wind speed and energy.The simulated biases in GCMs remain prominent,which induce a large uncertainty in the projected results.To reduce the uncertainties of projected near-surface wind speed(NSW)and better serve the wind energy development in Central Asia,the Weather Research and Forecasting(WRF)model with bias-corrected GCMs was employed.Compared with the outputs of GCMs,dynamical downscaling acquired using the WRF model can better capture the high-and low-value centres of NSWS,especially those of Central Asia's mountains.Meanwhile,the simulated NSWS bias was also reduced.For future changes in wind speed and wind energy,under the Representative Concentration Pathway 4.5(RCP4.5)scenario,NSWS during 2031-2050 is projected to decrease compared with that in 19862005.The magnitude of NSwS reduction during 2031-2050 willreach 0.1 m s^(-1).and the maximum reduction is projected to occur over the central and western regions(>0.2 m s^(-1)).Furthermore,future wind power density(WPD)can reveal nonstationarity and strong volatility,although a downward trend is expected during 2031-2050.In addition,the higher frequency of wind speeds at the turbine hub height exceeding 3.0 m s^(-1)can render the plain regions more suitable for wind energy development than the mountains from 2031 to 2050.This study can serve as a guide in gaining insights into future changes in wind energy across Central Asia and provide a scientific basis for decision makers in the formulation of policies for addressing climate change.

Quantifying the impacts of COVID-19 on Sustainable Development Goals using machine learning models

The COvID-19 pandemic has posed severe threats to global sustainable development.However,a comprehensive quantitative assessment of the impacts of COVID-19 on Sustainable Development Goals(SDGs)is still lacking.This research quantified the post-COVID-19 SDG progress from 2020 to 2024 using projected GDP growth and population and machine learning models including support vector machine,random forest,and extreme gradient boosting.The results show that the overall SDG performance declined by 7.7%in 2020 at the global scale,with 12 socioeconomic SDG performance decreasing by 3.0%-22.3%and 4 environmental SDG performance increasing by 1.6%-9.2%.By 2024,the progress of 12 SDGs will lag behind for one to eight years compared to their pre-COVID-19 trajectories,while extra time will be gained for 4 environment-related SDGs.Furthermore,the pandemic will cause more impacts on countries in emerging markets and developing economies than those on advanced economies,and the latter will recover more quickly to be closer to their pre-covID-19 trajectories by 2024.Post-COVID-19 economic recovery should emphasize in areas that can help decouple economic growth from negative environmental impacts.The results can help government and non-state stakeholders identify critical areas for targeted policy to resume and speed up the progress to achieve SDGs by 2030.

The rebound effect on energy efficiency improvements in China’s transportation sector:A CGE analysis

Energy use is becoming more efficient due to technological innovations.We focused on the transportation sector in China to develop a national multisector computable general equilibrium(CGE)model for analyzing the rebound effect from an improvement of 10%in the energy efficiency.We compared the size of the energy rebound effect at both the macroeconomic and sectoral levels in different transportation modal subsectors,namely rail,road,water,and air travel.The findings showed that the magnitude of the rebound effect varies across the transportation modes.This is particularly true for the air transportation sector,which has an economy-wide rebound effect of 30.1%and an own-sector rebound effect of 74.6%because of a sharp increase in the export demand for air transport services.We also quantitatively evaluated the contribution of energy efficiency improvement in the transportation sector to China’s economic growth and carbon reductions and found a positive dividend effect on the economy as well as the environment.The modeling results suggest that improving overall transportation energy efficiency by 10%generates an economy-wide welfare gain of approximately 29 billion yuan,while 19 billion yuan are attributable to a more efficient road transportation subsector.Furthermore,to offset the effects of these mode-specific rebound effects,we simulated the effectiveness of different policies and solutions.These included economic instruments in the form of energy,environmental,and carbon taxes,household transport consumption structure adjustments,and energy structure adjustments.This study revealed that combining these sustainable development policies offers opportunities for economy-wide multisectoral improvements in energy savings,emissions reduction,and economic benefits.

Revisiting BCC-SESM parameters sensitivity with BCC-CSM1.1 co_(2)-concentration-driven simulations

Based on the results of the complex climate model BCC-CSM,the Beijing Climate Center Simple Earth System Model(BCC-SESM)was developed for climate system simulations in Integrated Assessment Models(IAMs).The first version of the BCC-SESM model was based on a high-emissions scenario(ESMRCP8.5)and tends to overestimate the temperatures in low and medium emissions scenarios.To address this problem,this study uses three CO_(2)-concentration-driven simulations under different RCP scenarios of complex climate models to evaluate parameters sensitivity and their impacts on projection efficacy.The results show that the new version of the BCC-SESM(denoted as BCC-SESM1.1)model based on a medium-emissions scenario experiment(RCP4.5)is more suitable for temperature projections for various climate scenarios.It can well reproduce the original value of complex climate model.At the same time,it also has high predictive efficacies for medium(RCP4.5)and low(RCP2.6)emissions scenarios,although it tends to underestimate for high emissions scenario(RCP8.5).The sensitivity tests for different RCP scenarios shows that the BCC-SESM1.1 has higher efficacy in projections of future climate change than those model versions based on the other scenarios.The projection deviations for the global average temperature by the BCC-SESM1.1(<2%)are better than the previous BCC-SESM(<5%).In light of recent progress in climate policy,the BCC-SESM1.1 is hence more suitable for coupling with IAMs for the purposes of assessing climate outcomes.