Climate resilience
The formal definition of the term is the "capacity of social, economic and ecosystems to cope with a hazardous event or trend or disturbance".
For example, building a seawall to protect a coastal community from flooding might help maintain existing ways of life there.
There are also social and community approaches, as well as nature-based ones, for example by restoring ecosystems like forests to act as natural barriers against climate impacts.
Climate resilience is a broader concept that includes adaptation but also emphasizes a system-wide approach to managing risks.
Two examples for objective tools are the Resilience Index Measurement and Analysis (RIMA) and the Livelihoods Change Over Time (LCOT).
[6] Climate resilience is generally considered to be the ability to recover from, or to mitigate vulnerability to, climate-related shocks such as floods and droughts.
[7] It is a political process that strengthens the ability of all to mitigate vulnerability to risks from, and adapt to changing patterns in, climate hazards and variability.
[7] The IPCC Sixth Assessment Report considers climate resilience to be "the capacity of social, economic and ecosystems to cope with a hazardous event or trend or disturbance".
[1]: 7 Resilience is a useful concept because it speaks across sectors and disciplines but this also makes it open to interpretation resulting in differing, and at times competing, definitions.
Climate resilient development is a closely related area of work and research topic that has recently emerged.
[9]: 172 Currently, the majority of work regarding climate resilience has focused on actions taken to maintain existing systems and structures.
For example, building a seawall to protect a coastal community from flooding might help maintain existing ways of life there.
[9]: 179 However, transformations may fail if they do not integrate social justice, consider power differences and political inclusion, and if they do not deliver improvements in incomes and wellbeing for everyone.
Cities, states, and nations that have already developed such networks generally have far higher net incomes and gross domestic product (GDP).
On every continent, governments are adopting policies for climate resilient economies, driven in part by international frameworks such as the Paris Agreement and the Sustainable Development Goals.
[7] This needs to be coupled with greater consideration of the socio-ecological resilience and context-specific values of marginalized communities and meaningful engagement with the most vulnerable in decision making.
This approach helps farmers to adapt their agricultural methods (for raising livestock and crops) to the effects of climate change.
[27] Climate resilience in general is the ability to recover from, or to mitigate vulnerability to, climate-related shocks such as floods and droughts.
Examples are protecting larger areas of semi-natural habitat and creating links between parts of the landscape to help species move.
[34]: 283 At larger governmental levels, general programs to improve climate resiliency through greater disaster preparedness are being implemented.
This includes targeted identification of vulnerability hotspots, a better understanding of the drivers of resilience, and tools to infer the impact and effectiveness of resilience-building interventions.
Firstly, the definition of resilience is heavily contested, making it difficult to choose appropriate characteristics and indicators to track.
Creating mechanisms of adaptation that occur in isolation at local, state, or national levels may leave the overall social-ecological system vulnerable.
[41] Most of the recent initiatives to measure resilience in rural development contexts share two shortcomings: complexity and high cost.
[43] Most objective approaches use fixed and transparent definitions of resilience and allow for different groups of people to be compared through standardized metrics.
[6] Artificial intelligence (AI) plays a transformative role in enhancing climate resilience by enabling data-driven decision-making and predictive analytics.
By analyzing vast amounts of climate data, AI can identify patterns and trends that help communities anticipate and adapt to climate-related challenges such as extreme weather events, rising sea levels, and resource scarcity.
Machine learning algorithms can optimize resource allocation for disaster preparedness, improve agricultural practices to withstand climate variability, and enhance urban planning to build more resilient infrastructures.
[44][45] Through these applications, AI not only aids in mitigating the impacts of climate change but also empowers communities to develop sustainable strategies for long-term resilience.
For instance, building hospitals that can withstand natural disasters, roads that don't get washed away in the face of rains and floods.
