Climate change is a threat to the attainment of the Sustainable Development Goals(SDGs) in sub-Saharan Africa as its impacts can lead to increased incidences of poverty and inequality which can subsequently lead to a ...Climate change is a threat to the attainment of the Sustainable Development Goals(SDGs) in sub-Saharan Africa as its impacts can lead to increased incidences of poverty and inequality which can subsequently lead to a 12% decline in the Human Development Index(HDI) for subSaharan Africa. Emerging countries such as China have the potential to support Africa to achieve the SDGs by pioneering Southe South Climate Finance(SSCF) modalities. In order to increase knowledge on climate informed development and the role of China in global climate governance, the paper examined various research articles, case studies, policy briefs and project reports. Sino-African aid, investments and trade were noted as essential in mitigating Africa's climate change vulnerabilities which induce poverty traps and inequality. Some African countries were noted to have a comparative advantage in environmental standards over China but lacked the initiative to use this comparative advantage to enhance the Forum on Chinae Africa Cooperation(FOCAC) and assist China to have a sustainable growth trajectory. The paper concludes that SSCF modalities can enhance climate risk management in Africa if they focus on improving financial inclusion and improving climate finance flows towards climate change adaptation activities in Africa. Additionally, to increase the effectiveness and impact of Chinese climate finance support to Africa, African policymakers should not allow political and market forces to decide how climate related support from China should be allocated as decisions based on political and market forces could potentially promote an inequitable distribution of funds and ignore the most vulnerable countries and regions.展开更多
Open-access gridded climate products have been suggested as a potential source of data for index insurance design and operation in data-limited regions.However,index insurance requires climate data with long historica...Open-access gridded climate products have been suggested as a potential source of data for index insurance design and operation in data-limited regions.However,index insurance requires climate data with long historical records,global geographical coverage and fine spatial resolution at the same time,which is nearly impossible to satisfy,especially with open-access data.In this paper,we spatially downscaled gridded climate data(precipitation,temperature,and soil moisture)in coarse spatial resolution with globally available longterm historical records to finer spatial resolution,using satellite-based data and machine learning algorithms.We then investigated the effect of index insurance contracts based on downscaled climate data for hedging spring wheat yield.This study employed countylevel spring wheat yield data between 1982 and 2018 from 56 counties overall in Kazakhstan and Mongolia.The results showed that in the majority of cases(70%),hedging effectiveness of index insurances increases when climate data is spatially downscaled with a machine learning approach.These improvements are statistically significant(p≤0.05).Among other climate data,more improvements in hedging effectiveness were observed when the insurance design was based on downscaled temperature and precipitation data.Overall,this study highlights the reasonability and benefits of downscaling climate data for insurance design and operation.展开更多
Climate change has significantly increased the frequency and severity of extreme weather events,a trend recognized under the United Nations Sustainable Development Goal 13:Climate Action.This study forecasts hurricane...Climate change has significantly increased the frequency and severity of extreme weather events,a trend recognized under the United Nations Sustainable Development Goal 13:Climate Action.This study forecasts hurricane activity in the Yucatan Peninsula,Mexico,for the period 2025–2034 using advanced computational models,including Convolutional Neural Networks(CNNs),Long Short-Term Memory networks(LSTMs),Autoregressive Integrated Moving Average models(ARIMA),and Linear Regression(LR).Historical hurricane data were extracted from the HURDAT2 database kept by the National Hurricane Center(NHC)and spatially analyzed in QGIS to assess storm trajectories and wind intensities.The data were processed using Python,and each model was trained to predict hurricane frequency within three wind speed categories:<50 knots,50–100 knots,and>100 knots.Results reveal divergent performance among the models.CNN exhibited high variability for low-speed events,peaking at 4.21 events in 2027 and dropping to 1.27 by 2034.In contrast,LSTM and ARIMA maintained stable forecasts:LSTM fluctuated between 2.7 and 3.0,and ARIMA ranged from 1.5 to 1.8.For the 50–100 knot range,CNN reached an anomalous high of 8.14 events in 2032,while LSTM and ARIMA remained within narrower bands(1.85–2.01 and 1.32–1.99,respectively).At the>100 knot level,ARIMA showed a rising trend from 0.21 in 2025 to 0.57 in 2034,suggesting a potential increase in high-intensity cyclones.These findings emphasize the need for adaptive forecasting systems that account for nonlinear behavior under climate change conditions.The model outputs offer valuable insights for risk management,contingency planning,and infrastructure resilience in the hurricane-prone Yucatan Peninsula.展开更多
文摘Climate change is a threat to the attainment of the Sustainable Development Goals(SDGs) in sub-Saharan Africa as its impacts can lead to increased incidences of poverty and inequality which can subsequently lead to a 12% decline in the Human Development Index(HDI) for subSaharan Africa. Emerging countries such as China have the potential to support Africa to achieve the SDGs by pioneering Southe South Climate Finance(SSCF) modalities. In order to increase knowledge on climate informed development and the role of China in global climate governance, the paper examined various research articles, case studies, policy briefs and project reports. Sino-African aid, investments and trade were noted as essential in mitigating Africa's climate change vulnerabilities which induce poverty traps and inequality. Some African countries were noted to have a comparative advantage in environmental standards over China but lacked the initiative to use this comparative advantage to enhance the Forum on Chinae Africa Cooperation(FOCAC) and assist China to have a sustainable growth trajectory. The paper concludes that SSCF modalities can enhance climate risk management in Africa if they focus on improving financial inclusion and improving climate finance flows towards climate change adaptation activities in Africa. Additionally, to increase the effectiveness and impact of Chinese climate finance support to Africa, African policymakers should not allow political and market forces to decide how climate related support from China should be allocated as decisions based on political and market forces could potentially promote an inequitable distribution of funds and ignore the most vulnerable countries and regions.
基金supported by the German Federal Ministry of Education and Research(BMBF)[FKZ 01LZ1705A].
文摘Open-access gridded climate products have been suggested as a potential source of data for index insurance design and operation in data-limited regions.However,index insurance requires climate data with long historical records,global geographical coverage and fine spatial resolution at the same time,which is nearly impossible to satisfy,especially with open-access data.In this paper,we spatially downscaled gridded climate data(precipitation,temperature,and soil moisture)in coarse spatial resolution with globally available longterm historical records to finer spatial resolution,using satellite-based data and machine learning algorithms.We then investigated the effect of index insurance contracts based on downscaled climate data for hedging spring wheat yield.This study employed countylevel spring wheat yield data between 1982 and 2018 from 56 counties overall in Kazakhstan and Mongolia.The results showed that in the majority of cases(70%),hedging effectiveness of index insurances increases when climate data is spatially downscaled with a machine learning approach.These improvements are statistically significant(p≤0.05).Among other climate data,more improvements in hedging effectiveness were observed when the insurance design was based on downscaled temperature and precipitation data.Overall,this study highlights the reasonability and benefits of downscaling climate data for insurance design and operation.
文摘Climate change has significantly increased the frequency and severity of extreme weather events,a trend recognized under the United Nations Sustainable Development Goal 13:Climate Action.This study forecasts hurricane activity in the Yucatan Peninsula,Mexico,for the period 2025–2034 using advanced computational models,including Convolutional Neural Networks(CNNs),Long Short-Term Memory networks(LSTMs),Autoregressive Integrated Moving Average models(ARIMA),and Linear Regression(LR).Historical hurricane data were extracted from the HURDAT2 database kept by the National Hurricane Center(NHC)and spatially analyzed in QGIS to assess storm trajectories and wind intensities.The data were processed using Python,and each model was trained to predict hurricane frequency within three wind speed categories:<50 knots,50–100 knots,and>100 knots.Results reveal divergent performance among the models.CNN exhibited high variability for low-speed events,peaking at 4.21 events in 2027 and dropping to 1.27 by 2034.In contrast,LSTM and ARIMA maintained stable forecasts:LSTM fluctuated between 2.7 and 3.0,and ARIMA ranged from 1.5 to 1.8.For the 50–100 knot range,CNN reached an anomalous high of 8.14 events in 2032,while LSTM and ARIMA remained within narrower bands(1.85–2.01 and 1.32–1.99,respectively).At the>100 knot level,ARIMA showed a rising trend from 0.21 in 2025 to 0.57 in 2034,suggesting a potential increase in high-intensity cyclones.These findings emphasize the need for adaptive forecasting systems that account for nonlinear behavior under climate change conditions.The model outputs offer valuable insights for risk management,contingency planning,and infrastructure resilience in the hurricane-prone Yucatan Peninsula.
