Search

Identifying climate change risks, vulnerabilities of exposed populations, and implemented responses to climate change are crucial for developing effective strategies to mitigate climate hazards. However, existing climate change assessment indexes still have some limitations, such as insufficient consideration of policy response, limited coverage of countries, and lack of a multidimensional perspective. In this study, we developed a novel climate change impacts and responses (CCIR) index that incorporates comprehensive information on climate risks, disease burden, and mitigation actions in response to climate risks and damage. To our best knowledge, the CCIR index is the first of its kind that explores variations in climate risks, impacts, and responses across countries to identify vulnerabilities and find more targeted solutions. A positive correlation was found between the CCIR index and national Gross Domestic Product per capita, indicating that wealthier countries might allocate more resources toward mitigating climate impacts. Moreover, countries with better climate education tended to have a lower carbon footprint, highlighting the importance of climate education. Furthermore, countries with lower risks of emerging infectious diseases were more likely to consume more renewable energy. The results highlight the value of using a multidimensional CCIR framework to analyze the interactions among socioeconomic factors, environmental policies, and climate change risks in 158 countries. This comprehensive approach provides actionable insights to mitigate climate impacts and improve national climate resilience. It also streamlines monitoring efforts and promotes joint climate action across international boundaries. By identifying climate risks and opportunities, the CCIR index can help policymakers design, refine, and implement adaptation policies and measures to respond to the impacts of climate change. CCIR index evaluates countries' climate change mitigation and adaptation efforts. Wealthier countries invest more in mitigating climate change. Lower-income and coastal nations are vulnerable to climate change impacts. Countries at lower risk of emerging infectious diseases consume more renewable energy. Climate change education is associated with a lower carbon footprint. [ABSTRACT FROM AUTHOR]

Significant advances have been achieved in bilingual word-level alignment, yet the challenge remains for phrase-level alignment. Moreover, the need for parallel data is a critical drawback for the alignment task. This work proposes a system that alleviates these two problems: a unified phrase representation model using cross-lingual word embeddings as input and an unsupervised training algorithm inspired by recent works on neural machine translation. The system consists of a sequence-to-sequence architecture where a short sequence encoder constructs cross-lingual representations of phrases of any length, then an LSTM network decodes them w.r.t their contexts. After training with comparable corpora and existing key phrase extraction, our encoder provides cross-lingual phrase representations that can be compared without further transformation. Experiments on five data sets show that our method obtains state-of-the-art results on the bilingual phrase alignment task and improves the results of different length phrase alignment by a mean of 8.8 points in MAP. [ABSTRACT FROM AUTHOR]