TY - JOUR
T1 - Sustainability of Climate Change and Disaster Management Based on Greenhouse Gas Emissions Towards SDG13
T2 - Dynamic Analysis and Regional Comparison in China
AU - Ji, Li
AU - Tao, Shigui
AU - Lin, Tai Yu
AU - Sun, Yanan
AU - Chen, Mingle
AU - Chiu, Yung ho
AU - Yin, Yanxi
N1 - Publisher Copyright:
© 2025 ERP Environment and John Wiley & Sons Ltd.
PY - 2025/8
Y1 - 2025/8
N2 - Greenhouse gas (GHG) emissions continue to affect the climate; extreme weather events frequently exacerbate global climate risks, leading to significant reflections on climate change and sustainable development due to immense losses from natural disasters. This paper develops a dynamic three-stage network directional distance function (DDF) data envelopment analysis (DEA) model. The model is used to evaluate the efficiency of China's Sustainable Development Goal 13 (SDG13) and assess the efficiency across three stages: greenhouse gas (GHG) emissions, climate change, and disaster management. The innovation of this paper is that, compared to previous studies, the selected variables align more closely with the targets of SDG13, and the model is more systematic. Additionally, based on efficiency performance, provinces are categorized into four warning levels, and a comparative analysis is conducted by grouping the 30 provinces into seven geographic regions. This approach offers a more robust foundation for policymaker decision-making. Results indicate that China's SDG13 efficiency is concerning; GHG emissions perform best, followed by climate change, with disaster management being the weakest. Regionally, North and East China excel in green and low-carbon development and disaster management, while Northwest China demonstrates the poorest performance. The evaluation of Total-Factor Efficiency for key indicators revealed that CO2 and PM2.5 emission efficiencies are relatively acceptable. However, indicators reflecting the capacity to respond to extreme weather and natural disasters, such as the population affected and economic loss, demonstrate significant vulnerabilities.
AB - Greenhouse gas (GHG) emissions continue to affect the climate; extreme weather events frequently exacerbate global climate risks, leading to significant reflections on climate change and sustainable development due to immense losses from natural disasters. This paper develops a dynamic three-stage network directional distance function (DDF) data envelopment analysis (DEA) model. The model is used to evaluate the efficiency of China's Sustainable Development Goal 13 (SDG13) and assess the efficiency across three stages: greenhouse gas (GHG) emissions, climate change, and disaster management. The innovation of this paper is that, compared to previous studies, the selected variables align more closely with the targets of SDG13, and the model is more systematic. Additionally, based on efficiency performance, provinces are categorized into four warning levels, and a comparative analysis is conducted by grouping the 30 provinces into seven geographic regions. This approach offers a more robust foundation for policymaker decision-making. Results indicate that China's SDG13 efficiency is concerning; GHG emissions perform best, followed by climate change, with disaster management being the weakest. Regionally, North and East China excel in green and low-carbon development and disaster management, while Northwest China demonstrates the poorest performance. The evaluation of Total-Factor Efficiency for key indicators revealed that CO2 and PM2.5 emission efficiencies are relatively acceptable. However, indicators reflecting the capacity to respond to extreme weather and natural disasters, such as the population affected and economic loss, demonstrate significant vulnerabilities.
UR - https://www.scopus.com/pages/publications/85219591119
UR - https://www.scopus.com/pages/publications/85219591119#tab=citedBy
U2 - 10.1002/sd.3404
DO - 10.1002/sd.3404
M3 - Article
AN - SCOPUS:85219591119
SN - 0968-0802
VL - 33
SP - 5308
EP - 5326
JO - Sustainable Development
JF - Sustainable Development
IS - 4
ER -